A review of applications of metabolomics in cancer

Richard D Beger, Richard D Beger

Abstract

Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the "Warburg effect," advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient's response to medical interventions.

Figures

Figure 1
Figure 1
General flow chart of a typical metabolomics experiment in a cancer study.
Figure 2
Figure 2
Energy and metabolic pathways and associated protein enzymes and transporters active in cancer. Metabolite abbreviations: αKG, α-ketoglutarate; FBP, fructose 1,6-diphosphate; NADP, nicotinamide adenine dinucleotide phosphate; NADPH, reduced form of nicotinamide adenine dinucleotide phosphate; OAA, oxaloacetate; PEP, phosphoenol pyruvate; PYR, pyruvate. Protein abbreviations: ATPCL, ATP citrate lyase; CA, carbonic anhydrase; FASN, fatty acid synthase; GLUT, glucose transporter; GLNT, glutamine transporter; G6PD, glucose-6-phosphate dehydrogenase; HK, hexokinase; LDH, lactate dehydrogenase; MCT, monocarboxylate transporter; NHE1, Na+/H+ exchanger; PC, pyruvate carboxylase; PDH, pyruvate dehydrogenase; PK, pyruvate kinase; SDH, succinate dehydrogenase; TK/TA, transketolase/transaldolase.

References

    1. Fiehn O. Metabolomics—The link between genotypes and phenotypes. Plant Mol. Biol. 2002;48:155–171. doi: 10.1023/A:1013713905833.
    1. Clayton T.A., Lindon J.C., Cloarec O., Antti H., Charuel C., Hanton G., Provost J.P., Le Net J.L., Baker J.D., Walley R.J., et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature. 2006;440:1073–1077. doi: 10.1038/nature04648.
    1. Holmes E., Wilson I.D., Nicholson J.K. Metabolic phenotyping in health and disease. Cell. 2008;134:714–717. doi: 10.1016/j.cell.2008.08.026.
    1. Griffin J.L., Shockcor J.P. Metabolic profiles of cancer cells. Nat. Rev. Cancer. 2004;4:551–561. doi: 10.1038/nrc1390.
    1. Kim Y.S., Maruvada P., Milner J.A. Metabolomics in biomarker discovery: Future uses for cancer prevention. Future Oncol. 2008;4:93–102. doi: 10.2217/14796694.4.1.93.
    1. Spratlin J.L., Serkova N.J., Eckhardt S.G. Clinical applications of metabolomics in oncology: A review. Clin. Cancer Res. 2009;15:431–440. doi: 10.1158/1078-0432.CCR-08-1059.
    1. Fan T.W., Lane A.N., Higashi R.M. The promise of metabolomics in cancer molecular therapeutics. Curr. Opin. Mol. Ther. 2004;6:584–592.
    1. Chung Y.L., Griffiths J.R. Using metabolomics to monitor anticancer drugs. Ernst Schering Found. Symp. Proc. 2007;4:55–78.
    1. Vander Heiden M.G. Targeting cancer metabolism: A therapeutic window opens. Nat. Rev. Drug Discov. 2011;10:671–684. doi: 10.1038/nrd3504.
    1. Kaddurah-Daouk R., Kristal B.S., Weinshilboum R.M. Metabolomics: A global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxciol. 2008;48:653–683. doi: 10.1146/annurev.pharmtox.48.113006.094715.
    1. Warburg O. On the origin of cancer cells. Science. 1956;123:309–314.
    1. Gatenby R.A., Gillies R.J. Why do cancers have high aerobic glycolysis. Nat. Rev. Cancer. 2004;4:891–899. doi: 10.1038/nrc1478.
    1. Vander Heiden M.G., Cantley L.C., Thompson C.B. Understanding the warburg effect: The metabolic requirements of cell proliferation. Science. 2009;324:1029–1033. doi: 10.1126/science.1160809.
    1. Vizán P., Sánchez-Tena S., Alcarraz-Vizán G., Soler M., Messeguer R., Pujol M.D., Lee W.-N.P., Cascante M. Characterization of the metabolic changes underlying growth factor angiogenic activation: Identification of new potential therapeutic targets. Carcinogenesis. 2009;30:946–952. doi: 10.1093/carcin/bgp083.
    1. Israel M., Schwartz L. The metabolic advantage of tumor cells. Mol. Cancer. 2011;10:70. doi: 10.1186/1476-4598-10-70.
    1. Weljie A.M., Jirik F.R. Hypoxia-induced metabolic shifts in cancer cells: Moving beyond the Warburg effect. Int. J. Biochem. Cell Biol. 2011;43:981–989. doi: 10.1016/j.biocel.2010.08.009.
    1. Fiehn O. Combining genomics, metabolome analysis and biochemical modeling to understand metabolic networks. Int. J. Genomics. 2001;2:155–168.
    1. Nicholson J.K., Lindon J.C., Holmes E. Metabonomics: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological nmr data. Xenobiotica. 1999;29:1181–1189. doi: 10.1080/004982599238047.
    1. Nicholson J.K., Connelly J., Lindon J.C., Holmes E. Metabonomics: A platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 2002;1:153–161.
    1. Nicholson J.K., Holmes E., Wilson I.D. Gut microorganisms, mammalian metabolism and personalized health care. Nat. Rev. Micro. 2005;3:431–438. doi: 10.1038/nrmicro1152.
    1. Roessner U., Luedemann A., Brust D., Fiehn O., Linke T., Willmitzer L., Fernie A.R. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. The Plant Cell. 2001;13:11–29.
    1. Han X., Gross R.W. Global analyses of cellular lipidomes directly from crude extracts of biological samples by esi mass spectrometry: A bridge to lipidomics. J. Lipid Res. 2003;44:1071–1079. doi: 10.1194/jlr.R300004-JLR200.
    1. Wenk M.R. Lipidomics: New tools and applications. Cell. 2010;143:888–895. doi: 10.1016/j.cell.2010.11.033.
    1. Fernandis A.Z., Wenk M.R. Lipid-based biomarkers of cancer. J. Chrom. B. 2009;877:2830–2835. doi: 10.1016/j.jchromb.2009.06.015.
    1. Boros L.G., Brackett D.J., Harrigan G.G. Metabolic biomarker and kinase drug target discovery in cancer using stable isotope-based dynamic metabolic profiling (sidmap) Curr. Cancer Drug Targets. 2003;3:445–453. doi: 10.2174/1568009033481769.
    1. Boros L.G., Lerner M.R., Morgan D.L., Taylor S.L., Smith B.J., Postier R.G., Brackett D.J. [1,2–13c2]-d-glucose profiles of the serum, liver, pancreas, and dmba-induced pancreatic tumors of rats. Pancreas. 2005;31
    1. Lane N.L., Fan T.W.-H., Higashi R.M., Tan J., Bousamra M., Miller D.M. Prospects for clinical cancer metabolomics using stable isotope tracers. Exp. Mol. Pathol. 2009;86:165–173. doi: 10.1016/j.yexmp.2009.01.005.
    1. Zhang G.-F., Sadhukhan S., Tochtrop G.P., Brunengraber H. Metabolomics, pathway regulation, and pathway discovery. J. Biol. Chem. 2011;286:23631–23635.
    1. Beger R., Hansen D., Schnackenberg L., Cross B., Fatollahi J., Lagunero F.T., Sarnyai Z., Boros L. Single valproic acid treatment inhibits glycogen and rna ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the [u-13C6]-d-glucose tracer in mice. Metabolomics. 2009;5:336–345. doi: 10.1007/s11306-009-0159-1.
    1. Boros L.G., Lee W.-N.P., Cascante M. Imatinib and chronic-phase leukemias. N. Engl. J. Med. 2002;347:67–68. doi: 10.1056/NEJM200207043470116.
    1. Boros L.G. Metabolic targeted therapy of cancer: Current tracer technologies and future drug design strategies in the old metabolic network. Metabolomics. 2005;1:11–15. doi: 10.1007/s11306-005-1103-7.
    1. US Department of Health and Human Services, Food and Drug Administration; Rockville, MD, USA: 2006. Guidance for industry pharmacogenomic data submissions.
    1. Beger R., Colatsky T. Metabolomics data and the biomarker qualification process. Metabolomics. 2012;8:2–7. doi: 10.1007/s11306-011-0342-z.
    1. Griffin J., Nicholls A., Daykin C., Heald S., Keun H., Schuppe-Koistinen I., Griffiths J., Cheng L., Rocca-Serra P., Rubtsov D., et al. Standard reporting requirements for biological samples in metabolomics experiments: Mammalian/in vivo experiments. Metabolomics. 2007;3:179–188. doi: 10.1007/s11306-007-0077-z.
    1. Sumner L.W., Amberg A., Barrett D., Beger R., Beale M.H., Daykin C., Fan T.W., Fiehn O., Goodacre R., Griffin J.L., et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3:211–221. doi: 10.1007/s11306-007-0082-2.
    1. Rubtsov D., Jenkins H., Ludwig C., Easton J., Viant M., Günther U., Griffin J., Hardy N. Proposed reporting requirements for the description of nmr-based metabolomics experiments. Metabolomics. 2007;3:223–229. doi: 10.1007/s11306-006-0040-4.
    1. Goodacre R., Baker D.J., Beger R., Bessant C., Broadhurst D., Connor S., Capuani G., Craig A., Ebbels T., Kell D.B., et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics. 2007;3:231–241. doi: 10.1007/s11306-007-0081-3.
    1. Ganti S., Weiss R.H. Urine metabolomics for kidney cancer detection and biomarker discovery. Urol. Oncol. 2011;29:551–557. doi: 10.1016/j.urolonc.2011.05.013.
    1. Mamas M., Dunn W.B., Neyes L., Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch. Toxicol. 2010;85:5–17.
    1. Serkova N., Glunde K. Metabolomics of cancer. Methods Mol. Biol. 2009;250:273–295. doi: 10.1007/978-1-60327-811-9_20.
    1. Dunn W.B., Broadhurst D., Begley P., Zelena E., Francis-McIntyre S., Anderson N., Brown M., Knowles J.D., Halsall A., Haselden J.N., et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protocols. 2011;6:1060–1083. doi: 10.1038/nprot.2011.335.
    1. Dunn W.B., Ellis D.I. Metabolomics: Current analytical platforms and methodologies. Trends Anal. Chem. 2005;24:285–294. doi: 10.1016/j.trac.2004.11.021.
    1. Robertson D.G. Metabonomics in toxicology: A review. Toxicol. Sci. 2005;85:809–822. doi: 10.1093/toxsci/kfi102.
    1. Lenz E.M., Wilson I.D. Analytical strategies in metabonomics. J. Prot. Res. 2007;6:443–458. doi: 10.1021/pr0605217.
    1. Psychogios N., Hau D., Peng J., Guo A., Mandal R., Bouatra S., Sinelnikov I., Krishnamurthy R., Eisner R., Gautam B., et al. The human serum metabolome. PLoS One. 2011;6:e16957. doi: 10.1371/journal.pone.0016957.
    1. Sangster T., Major H., Plumb R., Wilson A.J., Wilson I.D. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst. 2006;131:1075–1078. doi: 10.1039/b604498k.
    1. Dunn W.B., Wilson I.D., Nicholls A.W., Broadhurst D. The importance of experimental design and qc samples in large-scale and ms-driven untargeted metabolomic studies of humans. Bioanalysis. 2012;4:2249–2264. doi: 10.4155/bio.12.204.
    1. Broadhurst D., Kell D. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics. 2006;2:171–196. doi: 10.1007/s11306-006-0037-z.
    1. Reily M.D., Robosky L.C., Manning M.L., Butler A., Baker J.D., Winters R.T. Dftmp, an NMR reagent for assessing the near-neutral pH of biological samples. J. Am. Chem. Soc. 2006;128:12360–12361. doi: 10.1021/ja063773h.
    1. Saude E., Sykes B. Urine stability for metabolomic studies: Effects of preparation and storage. Metabolomics. 2007;3:19–27. doi: 10.1007/s11306-006-0042-2.
    1. Katajamaa M., Orešič M. Data processing for mass spectrometry-based metabolomics. J. Chrom. A. 2007;1158:318–328.
    1. O'Sullivan A., Avizonis D., German J.B., Slupsky C.M. Software tools for NMR metabolomics. eMagRes. 2007
    1. Sugimoto M., Kawakami M., Robert M., Soga T., Tomita M. Bioinformatics tools for mass spectrometry-based metabolomics data processing and analysis. Curr. Bioinformatics. 2012;7:96–108.
    1. Fonville J.M., Richards S.E., Barton R.H., Boulange C.L., Ebbels T.M.D., Nicholson J.K., Holmes E., Dumas M.-E. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J. Chemometrics. 2010;24:636–649. doi: 10.1002/cem.1359.
    1. Madsen R., Lundstedt T., Trygg J. Chemometrics in metabolomics—a review in human disease diagnosis. Anal. Chim. Acta. 2010;659:23–33. doi: 10.1016/j.aca.2009.11.042.
    1. Wishart D.S., Knox C., Guo A.C., Eisner R., Young N., Gautam B., Hau D.D., Psychogios N., Dong E., Bouatra S., et al. Hmdb: A knowledgebase for the human metabolome. Nucl. Acids Res. 2009;37:D603–D610. doi: 10.1093/nar/gkn810.
    1. Wishart D.S., Tzur D., Knox C., Eisner R., Guo A.C., Young N., Cheng D., Jewell K., Arndt D., Sawhney S., et al. Hmdb: The human metabolome database. Nucl. Acids Res. 2007;35:D521–D526. doi: 10.1093/nar/gkl923.
    1. Kopka J., Schauer N., Krueger S., Birkemeyer C., Usadel B., Bergmuller E., Dormann P., Weckwerth W., Gibon Y., Stitt M., et al. Gmd@csb.Db: The golm metabolome database. Bioinformatics. 2005;21:1635–1638. doi: 10.1093/bioinformatics/bti236.
    1. Smith C.A., O’Maille G., Want E.J., Qin C., Trauger S.A., Brandon T.R., Custodio D.E., Abagyan R., Siuzdak G. Metlin—a metabolite mass spectral database. Ther. Drug Monit. 2005;27:747–751. doi: 10.1097/01.ftd.0000179845.53213.39.
    1. Sud M., Fahy E., Cotter D., Brown A., Dennis E.A., Glass C.K., Merrill A.H., Murphy R.C., Raetz C.R.H., Russell D.W., et al. Lmsd: Lipid maps structure database. Nucl. Acids Res. 2007;35:D527–D532. doi: 10.1093/nar/gkl838.
    1. Blekherman G., Laubenbacher R., Cortes D.F., Mendes P., Torti F.M., Akman S., Torti S.V., Shulaev V. Bioinformatics tools for cancer metabolomics. Metabolomics. 2011
    1. Yang C., Richardson A.D., Smith J.W., Osterman A. Comparative metabolomics of breast cancer. Pacific Symposium on Biocomputing. 2007:181–192.
    1. Lane A., Fan T.-M., Bousamra M., Higashi R., Yan J., Miller D. Stable isotope-resolved metabolomics (sirm) in cancer research with clinical application to nonsmall cell lung cancer. OMICS. 2011;15:173–182. doi: 10.1089/omi.2010.0088.
    1. Mamas M., Dunn W., Neyses L., Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch. Toxicol. 2011;85:5–17. doi: 10.1007/s00204-010-0609-6.
    1. Matheis K., Laurie D., Andriamandroso C., Arber N., Badimon L., Benain X., Bendjama K., Clavier I., Colman P., Firat H., et al. A generic operational strategy to qualify translational safety biomarkers. Drug Discov. Today. 2011;16:600–608. doi: 10.1016/j.drudis.2011.04.011.
    1. Johnson C.H., Patterson A.D., Krausz K.W., Lanz C., Kang D.W., Luecke H., Gonzalez F.J., Idle J.R. Radiation metabolomics. 4. UPLC-ESI-QTOFMS-based metabolomics for urinary biomarker discovery in gamma-irradiated rats. Radiation Res. 2011;175:473–484. doi: 10.1667/RR2437.1.
    1. Coy S.L., Cheema A.K., Tyburski J.B., Laiakis E.C., Collins S.P., Fornace A.J. Radiation metabolomics and its potential in biodosimetry. Int. J Rad. Bio. 2011;87:802–823. doi: 10.3109/09553002.2011.556177.
    1. O’Connell T., Ardeshirpour F., Asher S., Winnike J., Yin X., George J., Guttridge D., He W., Wysong A., Willis M., et al. Metabolomic analysis of cancer cachexia reveals distinct lipid and glucose alterations. Metabolomics. 2008;4:216–225. doi: 10.1007/s11306-008-0113-7.
    1. Seyfried T., Shelton L. Cancer as a metabolic disease. Nutr. Metab. 2010;7:7. doi: 10.1186/1743-7075-7-7.
    1. Kim J.-w., Dang C.V. Cancer's molecular sweet tooth and the Warburg effect. Cancer Res. 2006;66:8927–8930. doi: 10.1158/0008-5472.CAN-06-1501.
    1. Brown M., McDunn J., Gunst P., Smith E., Milburn M., Troyer D., Lawton K. Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies. Genome Medicine. 2012;4:33. doi: 10.1186/gm332.
    1. Kobayashi T., Nishiumi S., Ikeda A., Yoshie T., Sakai A., Matsubara A., Izumi Y., Tsumura H., Tsuda M., Nishisaki H., et al. A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiol. Biomarkers Prev. 2013
    1. Ikeda A., Nishiumi S., Shinohara M., Yoshie T., Hatano N., Okuno T., Bamba T., Fukusaki E., Takenawa T., Azuma T., et al. Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer. Biomed. Chromatogr. 2012;26:548–558. doi: 10.1002/bmc.1671.
    1. Odunsi K., Wollman R.M., Ambrosone C.B., Hutson A., McCann S.E., Tammela J., Geisler J.P., Miller G., Sellers T., Cliby W., et al. Detection of epithelial ovarian cancer using 1H-nmr-based metabonomics. Int. J. Cancer. 2005;113:782–788. doi: 10.1002/ijc.20651.
    1. Osl M., Drreiseitl S., Pfeifer B., Weinberger K., Klocker H., Bartsch G., Schafer G., Tilg B., Graber A. A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry. Bioinformatics. 2008;24:2908–2914. doi: 10.1093/bioinformatics/btn506.
    1. Gao P. C-myc suppression of mir-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 2009;458:762–765. doi: 10.1038/nature07823.
    1. Wang J., Yu L.F., Shen P., Wang S.F. Analysis of serum metabolome of patients with breast cancer by gas chromatography-mass spectrometry. Zhejiang Da Xue Bao Yi Xue Ban. 2009;38:478–484.
    1. Beger R., Schnackenberg L., Holland R., Li D., Dragan Y. Metabonomic models of human pancreatic cancer using 1d proton nmr spectra of lipids in plasma. Metabolomics. 2006;2:125–134. doi: 10.1007/s11306-006-0026-2.
    1. Yan S.K., Wei B.J., Lin Z.Y., Yang Y., Zhou Z.T., Zhang W.D. A metabonomic approach to the diagnosis of oral squamous cell carcinoma, oral clichen planus and oral leukoplakia. Oral Oncol. 2008;44:477–483. doi: 10.1016/j.oraloncology.2007.06.007.
    1. Kim R., Coates J., Bowles T., McNerney G., Sutcliffe J., Jung I., Gandour-Edwaeds R., Chuang F., Bold R., Kung H. Arginine deiminase as a novel therapy for prostate cancer induces autophary and caspase-independent apoptosis. Cancer Res. 2009;69:700–708. doi: 10.1158/0008-5472.CAN-08-3157.
    1. Nam H., Chung B.C., Kim Y., Lee K., Lee D. Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics. 2009;25:3151–3157. doi: 10.1093/bioinformatics/btp558.
    1. Ganti S., Weiss R.H. Urine metabolomics for kidney cancer detection and biomarker discovery. Metabolomics. 2011;29:551–557.
    1. Poli D., Carbognani P., Corradi M., Goldoni M., Acampa O., Balbi B., Bianchi L., Rusca M., Mutti A. Exhaled volatile organic compounds in patients with non-small cell lung cancer: Cross sectional and nested short-term follow-up study. Respir. Res. 2005;6:71. doi: 10.1186/1465-9921-6-71.
    1. Phillips M., Cataneo R.N., Ditkoff B.A., Fisher P., Greenberg J., Gunawardena R., Kwon C.S., Tietje O., Wong C. Prediction of breast cancer using volatile biomarkers in the breath. Breast Cancer Res. 2006;99:19–21. doi: 10.1007/s10549-006-9176-1.
    1. Nishiumi S., Shinohara M., Ikeda A., Yoshie T., Hatano N., Kakuyama S., Mizuno S., Sanuki T., Kutsumi H., Fukusaki E., et al. Serum metabolomics as a novel diagnostic approach for pancreatic cancer. Metabolomics. 2010;6:518–528. doi: 10.1007/s11306-010-0224-9.
    1. Kim J.W., Dang C.V. Cancer’s molecular sweet tooth and the Warburg effect. Cancer Res. 2006;66:8927–8930. doi: 10.1158/0008-5472.CAN-06-1501.
    1. Tannock I.F., Rotin D. Acid ph in tumors and its potential for therapeutic exploitation. Cancer Res. 1989;49:4373–4384.
    1. Zamecnik P.C., Loftfield R.B., Stephenson M.L., Steele J.M. Studies on the carbohydrate and protein metabolism of the rat hepatoma. Cancer Res. 1951;11:592–602.
    1. Lv W., Yang T. Identification of possible biomarkers for breast cancer from free fatty acid profiles determined by GC/MS and multivariate statistical analysis. Clin. Biochem. 2012;45:127–133. doi: 10.1016/j.clinbiochem.2011.10.011.
    1. Bhalla K., Hwang B.J., Dewi R.E., Ou L., Twaddel W., Fang H.-b., Vafai S.B., Vazquez F., Puigserver P., Boros L., et al. Pgc1a promotes tumor growth by inducing gene expression programs supporting lipogenesis. Cancer Res. 2011;71:6888–6898. doi: 10.1158/0008-5472.CAN-11-1011.
    1. Dang C.V. Glutaminolysis: Supplying carbon or nitrogen or both for cancer cells? Cell Cycle. 2010;9:3884–3886. doi: 10.4161/cc.9.19.13302.
    1. Carracedo A., Cantley L.C., Pandolfi P.P. Cancer metabolism: Fatty acid oxidation in the limelight. Nat. Rev. Cancer. 2013;13:227–232. doi: 10.1038/nrc3483.
    1. McKeehan W.L. Glycolysis, glutaminolysis and cell proliferation. Cell Biol. Int. Rep. 1982;6:635–650. doi: 10.1016/0309-1651(82)90125-4.
    1. Moreadith R.W., Lehninger A.L. The pathways of glutamate and glutamine oxidation by tumor cell mitochondria. Role of mitochondrial nad(p)+-dependent malic enzyme. J. Biol. Chem. 1984;259:6215–6221.
    1. Ben-Yoseph O., Badar-Goffer R.S., Morris P.G., Bachelard H.S. Glycerol 3-phosphate and lactate as indicators of the cerebral cytoplasmic redox state in severe and mild hypoxia respectively: A 13C- and 31P N. M. R. Study. Biochem. J. 1993;291:915–919.
    1. Griffiths J.R., Stubbs M. Opportunities for studying cancer by metabolomics: Preliminary observations on tumors deficient in hypoxia-inducible factor 1. Adv. Enzyme Regul. 2003;43:67–76. doi: 10.1016/S0065-2571(02)00030-4.
    1. Struck W., Waszczuk-Jankowska M., Kaliszan R., Markuszewski M.J. The state-of-the-art determination of urinary nucleosides using chromatographic techniques “Hyphenated” With advanced bioinformatics methods. Anal. Bioanal. Chem. 2011;410:2039–2050.
    1. Zambonin C.G., Aresta A., Palmisano F., Specchia G., Liso V. Liquid chromatography determination of urinary 5-methyl-2'-deoxycytidine and psuedouridine as potential biomarkers for leukaemia. J. Pharm. Biomed. Anal. 1999;21:1045–1051. doi: 10.1016/S0731-7085(99)00221-6.
    1. Sasco A.J., Rey F., Reynaud C., Bobin Y.L., Clavel M., Niveleau A. Breast cancer prognostic significance of some modified urinary nucleosides. Cancer Lett. 1996;108:157–162. doi: 10.1016/S0304-3835(96)04393-5.
    1. Zheng Y.F., Kong H.W., Xiong J.H., Lv S., Xu G.W. Clinical significance and prognostic value of urinary nucleosides in breast cancer patients. Clin. Biochem. 2005;38:24–30. doi: 10.1016/j.clinbiochem.2004.09.021.
    1. Woo H.M., Kim K.M., Choi M.H., Jung B.H., Lee J., Kong G., Nam S.J., Kim S., Bai S.W., Chung B.C. Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers. Clin. Chem. Acta. 2009;400:63–69. doi: 10.1016/j.cca.2008.10.014.
    1. Zheng Y.F., Yang J., Zhao X.J., Feng B., Kong H.W., Chen Y.J., Lv S., Zheng M.H., Xu G.W. Urinary nucleosides as biological markers for patients with colorectal cancer. World J. Gastroenterol. 2005;11:3871–3876.
    1. Yang J., Xu G., Zheng Y., Kong H., Pang T., Lv S., Yang Q. Diagnosis of liver cancer using hplc-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases. J. Chrom. B. 2004;813:59–65. doi: 10.1016/j.jchromb.2004.09.032.
    1. Sreekumar A., Poisson L.M., Rajendiran T.M., Khan A.P., Cao Q., Yu J., Laxman B., Mehra R., Lonigro R.J., Li Y., et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457:910–914. doi: 10.1038/nature07762.
    1. Olson R.E. Oxidation of C14-labeled carbohydrate intermediates in tumor and normal tissue. Cancer Res. 1951;11:571–584.
    1. Ackerstaff E., Pflug B.R., Nelson J.B., Bhujwalla Z.M. Detection of increased choline compounds with proton nuclear magnetic resonance spectroscopy subsequent to malignant transformation of human prostatic epithelial cells. Cancer Res. 2001;61:3599–3603.
    1. Glunde K., Jie C., Bhujwalla Z.M. Molecular causes of the aberrant choline phospholipid metabolism in breast cancer. Cancer Res. 2004;64:4270–4276. doi: 10.1158/0008-5472.CAN-03-3829.
    1. Hilvo M., Denkert C., Lehtinen L., Müller B., Brockmöller S., Seppänen-Laakso T., Budczies J., Bucher E., Yetukuri L., Castillo S., et al. Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res. 2011;71:3236–3245. doi: 10.1158/0008-5472.CAN-10-3894.
    1. Dong J., Cai X., Zhao L., Xue X., Zou L., Zhang X., Liang X. Lysophosphatidylcholine profiling of plasma: Discrimination of isomers and discovery of lung cancer biomarkers. Metabolomics. 2010;6:478–488. doi: 10.1007/s11306-010-0215-x.
    1. Patterson A.D., Maurhofer O., Beyoğlu D., Lanz C., Krausz K.W., Pabst T., Gonzalez F.J., Dufour J.-F.o., Idle J.R. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res. 2011;71:6590–6600. doi: 10.1158/0008-5472.CAN-11-0885.
    1. Meleh M., Pozlep B., Mlakar A., Meden-Vrtovec H., Zupanic-Kralj L. Determination of serum lysophosphatidic acid as a potential biomarker for ovarian cancer. J. Chrom. B. 2007;858:287–291. doi: 10.1016/j.jchromb.2007.08.008.
    1. Ringel M.D., Hayre N., Saito J., Saunier B., Schuppert F., Burch H., Bernet V., Burman K.D., Kohn L.D., Saji M. Overexpression and overactivation of akt in thyroid carcinoma. Cancer Res. 2001;61:6105–6111.
    1. Vivanco I., Sawyers C.L. The phosphatidylinositol 3-kinase-akt pathway in human cancer. Nat. Rev. Cancer. 2002;2:489–501. doi: 10.1038/nrc839.
    1. Fernandis A.Z., Wenk M.R. Lipid-based biomarkers for cancer. J. Chrom. B. 2009;877:2830–2835. doi: 10.1016/j.jchromb.2009.06.015.
    1. Saddoughi S.A., Song P., Ogretmen B. Roles of bioactive sphingolipids and cancer biology and therapeutics. Subcell. Biochem. 2008;49:413–440. doi: 10.1007/978-1-4020-8831-5_16.
    1. Nava V.E., Hobson J.P., Murthy S., Milstien S., Spiegel S. Sphingosine kinase type 1 promotes estrogen-dependent tumorigenesis of breast cancer mcf-7 cells. Exp. Cell Res. 2002;281:115–127. doi: 10.1006/excr.2002.5658.
    1. Sarkar S., Maceyka M., Hait N.C., Paugh S.W., Sankala H., Milstien S., Spiegel S. Sphingosine kinase 1 is required for migration, proliferation and survival of mcf-7 human breast cancer cells. FEBS Lett. 2005;579:5313–5317. doi: 10.1016/j.febslet.2005.08.055.
    1. Poullet J.-B., Martinez-Bisbal M., Valverde D., Monleon D., Celda B., Arus C., Van Huffel S. Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007:5407–5410.
    1. Tessem M.B. Evaluation of lactate and alanine as metabolic biomarkers of prostate cancer using 1H HR-MAS spectroscopy of biopsy tissues. J Magn. Reson. Med. 2008;60:510–516. doi: 10.1002/mrm.21694.
    1. Sitter B., Sonnewald U., Spraul M., Fjösne H.E., Gribbestad I.S. High-resolution magic angle spinning mrs of breast cancer tissue. NMR Biomed. 2002;15:327–337. doi: 10.1002/nbm.775.
    1. Wilson M., Davies N.P., Brunder M.-A., McConville C., Grundy R.G., Peet A.C. High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumors. Mol. Cancer. 2009;8:6. doi: 10.1186/1476-4598-8-6.
    1. Somashekar B.S., Kamarajan P., Danciu T., Kapila Y.L., Chinnaiyan A.M., Rajendiran T.M., Ramamoorthy A. Magic angle spinning NMR-based metabolic profiling of head and neck squamous cell carcinoma tissues. J. Prot. Res. 2011;10:5232–5241. doi: 10.1021/pr200800w.
    1. Stenman K., Stattin Pär., Stenlund H., Riklund K., Gröbner G., Bergh A. 1H hrmas nmr derived bio-markers related to tumor grade, tumor cell fraction, and cell proliferation in prostate tissue samples. Biomarker Insights. 2011;6:39–47.
    1. Cheng L.L., Burns M.A., Taylor J.L., He W., Halpern E.F., McDougal W.S., Wu C.-L. Metabolic characterization of human prostate cancer with tissue magnetic resonance spectroscopy. Cancer Res. 2005;65:3030–3034.
    1. Chan E.C.Y., Koh P.K., Mal M., Cheah P.Y., Eu K.W., Backshall A., Cavill R., Nicholson J.K., Keun H.C. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS) J. Prot. Res. 2008;8:352–361.
    1. Brindle K. New approaches for imaging tumour responses to treatment. Nat. Rev. Cancer. 2008;8:94–107. doi: 10.1038/nrc2289.
    1. Friess H., Langhans J., Ebert M., Beger H.G., Stollfuss J., Reske S.N., Büchler M.W. Diagnosis of pancreatic cancer by 2 [18-F]-fluoro-2-deoxy-D-glucose positron emission tomography. Gut. 1995;36:771–777. doi: 10.1136/gut.36.5.771.
    1. Pöttgen C., Levegrün S., Theegarten D., Marnitz S., Grehl S., Pink R., Eberhardt W., Stamatis G., Gauler T., Antoch G., et al. Value of 18f-fluoro-2-deoxy-d-glucose-positron emission tomography/computed tomography in non-small-cell lung cancer for prediction of pathologic response and times to relapse after neoadjuvant chemoradiotherapy. Clin. Cancer Res. 2006;12:97–106. doi: 10.1158/1078-0432.CCR-05-0510.
    1. Haddadin I.S., McIntosh A., Meisamy S., Corum C., Snyder A.L.S., Powell N.J., Nelson M.T., Yee D., Garwood M., Bolan P.J. Metabolite quantification and high-field mrs in breast cancer. NMR Biomed. 2009;22:65–76. doi: 10.1002/nbm.1217.
    1. Towner R.A., Foley L.M., Painter D.M. Hepatocarcinogenesis tumor grading correlated within vivo image-guided 1H-nmr spectroscopy in a rat model. Toxicol. Appl. Pharmacol. 2005;207(Suppl2):237–244. doi: 10.1016/j.taap.2005.02.035.
    1. Carroll P., Coakley F., Kurhanewicz J. Magnetic resonance imaging and spectroscopy of prostate cancer. Rev. Urol. 2006;8:S4–S10.
    1. Yokota H., Guo J., Matoba M., Higashi K., Tonami H., Nagao Y. Lactate, choline, and creatine levels measured by vitro 1H-MRS as prognostic parameters in patients with non-small-cell lung cancer. J. Magn. Reson. Imaging. 2007;25:992–999. doi: 10.1002/jmri.20902.
    1. Law M. Advanced imaging techniques in brain tumors. Cancer Imaging. 2009;9:S4–S9. doi: 10.1102/1470-7330.2009.9002.
    1. Türkbey B., Aras Ö., Karabulut N., Tuncay Turgut A., Akpinar E., Alibek S., Pang Y., Ertürk S., El Khouli R., Bluemke D., et al. Diffusion-weighted mri for detecting and monitoring cancer: A review of current applications in body imaging. Diagn. Interv. Radiol. 2012;18:46–59.
    1. Türkbey B., Thomasson D., Bernardo M., Choyke P.L. The role of dynamic contrast-enhanced mri in cancer diagnosis and treatment. Diagn. Interv. Radiol. 2010;16:186–192.
    1. DeMartini W., Lehman C., Partridge S. Breast mri for cancer detection and characterization: A review of evidence-based clinical applications. Acad, Radiol. 2008;15:408–416. doi: 10.1016/j.acra.2007.11.006.
    1. Warner E., Messersmith H., Causer P., Eisen A., Shumak R., Plewes D. Systematic review: Using magnetic resonance imaging to screen women at high risk for breast cancer. Ann. Intern. Med. 2008;148:671–679. doi: 10.7326/0003-4819-148-9-200805060-00007.
    1. Bartella L., Huang W. Proton (1H) MR spectroscopy of the breast. Radiographics. 2007;27:S241–S252. doi: 10.1148/rg.27si075504.
    1. Dowling C., Bollen A.W., Noworolski S.M., McDermott M.W., Barbaro N.M., Day M.R., Henry R.G., Chang S.M., Dillon W.P., Nelson S.J., et al. Preoperative proton mr spectroscopic imaging of brain tumors: Correlation with histopathologic analysis of resection specimens. Am. J. Neuroradiol. 2001;22:604–612.
    1. Seitz M., Shukla-Dave A., Bjartell A., Touijer K., Sciarra A., Bastian P.J., Stief C., Hricak H., Graser A. Functional magnetic resonance imaging in prostrate cancer. Europ. Urol. 2009;55:801–814. doi: 10.1016/j.eururo.2009.01.027.
    1. Alusta P., Im I., Pearce B.A., Beger R.D., Kretzer R.M., Buzatu D.A., Wilkes J.G. Improving proton mr spectroscopy of brain tissue for noninvasive diagnostics. J. Magn. Reson. Imaging. 2010;32:818–829. doi: 10.1002/jmri.22332.
    1. Elion G.B., Singer S., Hitchings G.H. Antagonists of nucleic acid derivatives: Viii. Synergism in combinations of biochemically related antimetabolites. J. Biol. Chem. 1954;208:477–488.
    1. Yauch R.L., Settleman J. Recent advances in pathway-targeted cancer drug therapies emerging from cancer genome analysis. Curr. Opin. Genet. Dev. 2012;22:45–49. doi: 10.1016/j.gde.2012.01.003.
    1. Tennant D.A., Duran R.V., Gottlieb E. Targeting metabolic transformation for cancer therapy. Nat. Rev. Cancer. 2010;10:267–277. doi: 10.1038/nrc2817.
    1. Weiss R.H., Kim K. Metabolomics in the study of kidney diseases. Nat. Rev. Nephrol. 2012;8:22–33. doi: 10.1038/nrneph.2011.152.
    1. Bayet-Robert M., Morvan D., Chollet P., Barthomeuf C. Pharmacometabolomics of docetaxel-treated human mcf7 breast cancer cells provides evidence of varying cellular responses at high and low doses. Breast Cancer Res. Treat. 2010;120:613–626. doi: 10.1007/s10549-009-0430-1.
    1. Backshall A., Sharma R., Clarke S.J., Keun H.C. Pharmacometabonomic profiling as a predictor of toxicity in pateints with inoperable colorectal cancer treated with capecitabine. Clin. Cancer Res. 2011;17:3019–3028. doi: 10.1158/1078-0432.CCR-10-2474.
    1. Evelhoch J., Garwood M., Vigneron D., Knopp M., Sullivan D., Menkens A., Clarke L., Liu G. Expanding the use of magnetic resonance in the assessment of tumor response to therapy: Workshop report. Cancer Res. 2005;65:7041–7044. doi: 10.1158/0008-5472.CAN-05-0674.
    1. Zerhouni E.A., Sanders C.A., von Eschenbach A.C. The biomarkers consortium: Public and private sectors working in partnership to improve the public health. The Oncologist. 2007;12:250–252. doi: 10.1634/theoncologist.12-3-250.
    1. Goodsaid F.M., Mendrick D.L. Translational medicine and the value of biomarker qualification. Sci. Transl. Med. 2010;2:47ps44–47ps44. doi: 10.1126/scitranslmed.3001040.
    1. Muirhead L.J., Kinross J., FitzMaurice T.S., Takats Z., Darzi A., Nicholson J.K. Surgical systems biology and personalized longitudinal phenotyping in critical care. Pers. Med. 2012;9:593–608. doi: 10.2217/pme.12.70.
    1. Nicholson J.K., Holmes E., Kinross J.M., Darzi A.W., Takats z., Lindon J.C. Metabolic phenotyping in clinical and surgical environments. Nature. 2012;491:384–392. doi: 10.1038/nature11708.
    1. Balog J., Szaniszlo T., Schaefer K.-C., Denes J., Lopata A., Godorhazy L., Szalay D., Balogh L., Sasi-Szabo L., Toth M., et al. Identification of biological tissues by rapid evaporative ionization mass spectrometry. Anal. Chem. 2010;82:7343–7350. doi: 10.1021/ac101283x.
    1. Oermann E.K., Wu J., Guan K.-L., Xiong Y. Alterations of metabolic genes and metabolites in cancer. Semin. Cell Dev. Biol. 2012;23:370–380. doi: 10.1016/j.semcdb.2012.01.013.
    1. Singh A., Happel C., Manna S., Acquaah-Mensah G., Carratero J., Kumar S., Nasipuri P., Krausz K., Wakabayashi N., Ruby Dewi R., et al. Nrf2 regulates mir-1 and mir-206 to drive tumorigenesis. J. Clin. Invest. 2013 in press.
    1. Bertilsson H., Tessem M.-B., Flatberg A., Viset T., Gribbestad I., Angelsen A., Halgunset J. Changes in gene transcription underlying the aberrant citrate and choline metabolism in human prostate cancer samples. Clin. Cancer Res. 2012;18:3261–3269. doi: 10.1158/1078-0432.CCR-11-2929.
    1. Rantalainen M., Cloarec O., Beckonert O., Wilson I.D., Jackson D., Tonge R., Rowlinson R., Rayner S., Nickson J., Wilkinson R.W., et al. Statistically integrated metabonomic—Proteomic studies on a human prostate cancer xenograft model in mice. J. Prot. Res. 2006;5:2642–2655. doi: 10.1021/pr060124w.
    1. Ma Y., Zhang P., Wang F., Liu W., Yang J., Qin H. An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer. Ann. Surg. 2012;255:720–730. doi: 10.1097/SLA.0b013e31824a9a8b.
    1. Cuperlovic-Culf M., Ferguson D., Culf A., Morin P., Touaibia M. 1H nmr metabolomics analysis of glioblastoma subtypes: Correlation between metabolomics and gene expression characteristics. J. Biol. Chem. 2012;287:20164–20175.
    1. Eckhart A.D., Beebe K., Milburn M. Metabolomics as a key integrator for "Omic" advancement of personalized medicine and future therapies. Clin. Transl. Sci. 2012;5:285–288. doi: 10.1111/j.1752-8062.2011.00388.x.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever