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The Role of Metabolomics in the Study of Cancer Biomarkers and in the Development of Diagnostic Tools

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Advances in Cancer Biomarkers

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 867))

Abstract

This chapter introduces the emerging field of metabolomics and its application in the context of cancer biomarker research. Taking advantage of modern high-throughput technologies, and enhanced computational power, metabolomics has a high potential for cancer biomarker identification and the development of diagnostic tools. This chapter describes current metabolomics technologies used in cancer research, starting with metabolomics sample preparation, elaborating on current analytical methodologies for metabolomics measurement and introducing existing software for data analysis. The last part of this chapter deals with the statistical analysis of very large metabolomics datasets and their relevance for cancer biomarker identification.

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References

  1. Koppenol WH, Bounds PL, Dang CV (2011) Otto Warburg’s contributions to current concepts of cancer metabolism. Nat Rev Cancer 11:325–337

    Article  CAS  PubMed  Google Scholar 

  2. Warburg O (1956) On the origin of cancer cells. Science 123:309–314

    Article  CAS  PubMed  Google Scholar 

  3. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029–1033

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Lu W, Bennett BD, Rabinowitz JD (2008) Analytical strategies for LC-MS-based targeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 871:236–242

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Ludwig C, Ward DG, Martin A, Viant MR, Ismail T, Johnson PJ, Wakelam MJ, Gunther UL (2009) Fast targeted multidimensional NMR metabolomics of colorectal cancer. Magn Reson Chem 47(Suppl 1):S68–S73

    Article  CAS  PubMed  Google Scholar 

  6. Villas-Boas SG, Mas S, Akesson M, Smedsgaard J, Nielsen J (2005) Mass spectrometry in metabolome analysis. Mass Spectrom Rev 24:613–646

    Article  CAS  PubMed  Google Scholar 

  7. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S et al (2007) HMDB: the human metabolome database. Nucleic Acids Res 35:D521–D526

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Lu J-J, Bao J-L, Wu G-S, Xu W-S, Huang M-Q, Chen X-P, Wang Y-T (2013) Quinones derived from plant secondary metabolites as anti-cancer agents. Anticancer Agents Med Chem 13:456–463

    CAS  PubMed  Google Scholar 

  9. Bothwell JH, Griffin JL (2011) An introduction to biological nuclear magnetic resonance spectroscopy. Biol Rev Camb Philos Soc 86:493–510

    Article  PubMed  Google Scholar 

  10. Bathe OF, Shaykhutdinov R, Kopciuk K, Weljie AM, McKay A, Sutherland FR, Dixon E, Dunse N, Sotiropoulos D, Vogel HJ (2011) Feasibility of identifying pancreatic cancer based on serum metabolomics. Cancer Epidemiol Biomarkers Prev 20:140–147

    Article  CAS  PubMed  Google Scholar 

  11. Monleon D, Morales JM, Barrasa A, Lopez JA, Vazquez C, Celda B (2009) Metabolite profiling of fecal water extracts from human colorectal cancer. NMR Biomed 22:342–348

    Article  CAS  PubMed  Google Scholar 

  12. Tiziani S, Lopes V, Gunther UL (2009) Early stage diagnosis of oral cancer using 1H NMR-based metabolomics. Neoplasia 11:269–276, 264p following 269

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Weljie AM, Bondareva A, Zang P, Jirik FR (2011) 1H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system. J Biomol NMR 49:185–193

    Article  CAS  PubMed  Google Scholar 

  14. Villas-Boas SG, Nielsen J, Smedsgaard J, Hansen MA, Roessner-Tunali U (2007) Metabolome analysis: an introduction, vol 24. Wiley, Hoboken

    Book  Google Scholar 

  15. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM (2005) HPLC-MS-based methods for the study of metabonomics. J Chromatogr B 817:67–76

    Article  CAS  Google Scholar 

  16. Smith RD, Loo JA, Edmonds CG, Barinaga CJ, Udseth HR (1990) New developments in biochemical mass spectrometry: electrospray ionization. Anal Chem 62:882–899

    Article  CAS  PubMed  Google Scholar 

  17. March RE (1997) An introduction to quadrupole ion trap mass spectrometry. J Mass Spectrom 32:351–369

    Article  CAS  Google Scholar 

  18. Hu Q, Noll RJ, Li H, Makarov A, Hardman M, Graham Cooks R (2005) The Orbitrap: a new mass spectrometer. J Mass Spectrom 40:430–443

    Article  CAS  PubMed  Google Scholar 

  19. Amster IJ (1996) Fourier transform mass spectrometry. J Mass Spectrom 31:1325–1337

    Article  CAS  Google Scholar 

  20. Zhang G, Panigrahy D, Mahakian LM, Yang J, Liu JY, Stephen Lee KS, Wettersten HI, Ulu A, Hu X, Tam S et al (2013) Epoxy metabolites of docosahexaenoic acid (DHA) inhibit angiogenesis, tumor growth, and metastasis. Proc Natl Acad Sci U S A 110:6530–6535

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Bernini P, Bertini I, Luchinat C, Nincheri P, Staderini S, Turano P (2011) Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J Biomol NMR 49:231–243

    Article  CAS  PubMed  Google Scholar 

  22. de Jonge LP, Douma RD, Heijnen JJ, van Gulik WM (2012) Optimization of cold methanol quenching for quantitative metabolomics of Penicillium chrysogenum. Metabolomics 8:727–735

    Article  PubMed Central  PubMed  Google Scholar 

  23. Sellick CA, Knight D, Croxford AS, Maqsood AR, Stephens GM, Goodacre R, Dickson AJ (2010) Evaluation of extraction processes for intracellular metabolite profiling of mammalian cells: matching extraction approaches to cell type and metabolite targets. Metabolomics 6:427–438

    Article  CAS  Google Scholar 

  24. Yu Z, Kastenmüller G, He Y, Belcredi P, Möller G, Prehn C, Mendes J, Wahl S, Roemisch-Margl W, Ceglarek U (2011) Differences between human plasma and serum metabolite profiles. PLoS One 6:e21230

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Li S, Guo B, Song J, Deng X, Cong Y, Li P, Zhao K, Liu L, Xiao G, Xu F (2013) Plasma choline-containing phospholipids: potential biomarkers for colorectal cancer progression. Metabolomics 9:202–212

    Article  CAS  Google Scholar 

  26. Nishiumi S, Kobayashi T, Ikeda A, Yoshie T, Kibi M, Izumi Y, Okuno T, Hayashi N, Kawano S, Takenawa T (2012) A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS One 7:e40459

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Ganti S, Taylor SL, Kim K, Hoppel CL, Guo L, Yang J, Evans C, Weiss RH (2012) Urinary acylcarnitines are altered in human kidney cancer. Int J Cancer 130:2791–2800

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Xie GX, Chen TL, Qiu YP, Shi P, Zheng XJ, Su MM, Zhao AH, Zhou ZT, Jia W (2012) Urine metabolite profiling offers potential early diagnosis of oral cancer. Metabolomics 8:220–231

    Article  CAS  Google Scholar 

  29. Takeda I, Stretch C, Barnaby P, Bhatnager K, Rankin K, Fu H, Weljie A, Jha N, Slupsky C (2009) Understanding the human salivary metabolome. NMR Biomed 22:577–584

    Article  CAS  PubMed  Google Scholar 

  30. de Weerth C, Jansen J, Vos MH, Maitimu I, Lentjes EG (2007) A new device for collecting saliva for cortisol determination. Psychoneuroendocrinology 32:1144–1148

    Article  PubMed  Google Scholar 

  31. Wei J, Xie G, Zhou Z, Shi P, Qiu Y, Zheng X, Chen T, Su M, Zhao A, Jia W (2011) Salivary metabolite signatures of oral cancer and leukoplakia. Int J Cancer 129:2207–2217

    Article  CAS  PubMed  Google Scholar 

  32. Crews B, Wikoff WR, Patti GJ, Woo H-K, Kalisiak E, Heideker J, Siuzdak G (2009) Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data. Anal Chem 81:8538–8544

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Nakamizo S, Sasayama T, Shinohara M, Irino Y, Nishiumi S, Nishihara M, Tanaka H, Tanaka K, Mizukawa K, Itoh T (2013) GC/MS-based metabolomic analysis of cerebrospinal fluid (CSF) from glioma patients. J Neuro Oncol 113(1):65–74

    Google Scholar 

  34. Pedrioli PG, Eng JK, Hubley R, Vogelzang M, Deutsch EW, Raught B, Pratt B, Nilsson E, Angeletti RH, Apweiler R (2004) A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22:1459–1466

    Article  CAS  PubMed  Google Scholar 

  35. Hiller K, Hangebrauk J, Jäger C, Spura J, Schreiber K, Schomburg D (2009) MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal Chem 81:3429–3439

    Article  CAS  PubMed  Google Scholar 

  36. Stein SE (1999) An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J Am Soc Mass Spectrom 10:770–781

    Article  CAS  Google Scholar 

  37. Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (2012) Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform 7:96

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Kovàts ES (1961) Zusammenhänge zwischen Struktur und gasehromatographischen Daten organischer Verbindungen. Fresenius J Anal Chem 181:351–364

    Article  Google Scholar 

  39. Izquierdo-García J, Rodríguez I, Kyriazis A, Villa P, Barreiro P, Desco M, Ruiz-Cabello J (2009) A novel R-package graphic user interface for the analysis of metabonomic profiles. BMC Bioinformatics 10:363

    Article  PubMed Central  PubMed  Google Scholar 

  40. Benton H, Wong D, Trauger S, Siuzdak G (2008) XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 80:6382–6389

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Biswas A, Mynampati KC, Umashankar S, Reuben S, Parab G, Rao R, Kannan VS, Swarup S (2010) MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation. Bioinformatics 26:2639–2640

    Article  CAS  PubMed  Google Scholar 

  42. Lommen A (2009) MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81:3079–3086

    Article  CAS  PubMed  Google Scholar 

  43. Bunk B, Kucklick M, Jonas R, Münch R, Schobert M, Jahn D, Hiller K (2006) MetaQuant: a tool for the automatic quantification of GC/MS-based metabolome data. Bioinformatics 22:2962–2965

    Article  CAS  PubMed  Google Scholar 

  44. Melamud E, Vastag L, Rabinowitz JD (2010) Metabolomic analysis and visualization engine for LC−MS data. Anal Chem 82:9818–9826

    Article  CAS  PubMed  Google Scholar 

  45. Babushok V, Linstrom P, Reed J, Zenkevich I, Brown R, Mallard W, Stein S (2007) Development of a database of gas chromatographic retention properties of organic compounds. J Chromatogr A 1157:414–421

    Article  CAS  PubMed  Google Scholar 

  46. Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S, Fiehn O (2009) FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 81:10038–10048

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmüller E, Dörmann P, Weckwerth W, Gibon Y, Stitt M (2005) GMD@ CSB. DB: the Golm metabolome database. Bioinformatics 21:1635–1638

    Article  CAS  PubMed  Google Scholar 

  48. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27:747–751

    Article  CAS  PubMed  Google Scholar 

  49. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142

    Article  PubMed Central  PubMed  Google Scholar 

  50. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learnin. Springer, New York

    Book  Google Scholar 

  51. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York

    Google Scholar 

  52. Katajamaa M, Orešič M (2007) Data processing for mass spectrometry-based metabolomics. J Chromatogr A 1158:318–328

    Article  CAS  PubMed  Google Scholar 

  53. Lee JJA, Verleysen M (2007) Nonlinear dimensionality reduction. Springer, New York/London

    Book  Google Scholar 

  54. Van der Maaten, LJP (2013) Barnes-Hut SNE. In: Proceedings of the international conference on learning representations, Scottsdale, Arizona (USA)

    Google Scholar 

  55. Bottou L (2010) Large-Scale machine learning with stochastic gradient descent. In: Proceedings of the 19th international conference on computational statistics, Paris (France)

    Google Scholar 

  56. Koh K, Kim S-J, Boyd SP (2007) An interior-point method for large-scale l1-regularized logistic regression. J Mach Learn Res 8:1519–1555

    Google Scholar 

  57. Vapnik V (1995) The nature of statistical learn theory. Springer, New York

    Book  Google Scholar 

  58. Goodacre R, Broadhurst D, Smilde AK, Kristal BS, Baker JD, Beger R, Bessant C, Connor S, Capuani G, Craig A (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3:231–241

    Article  CAS  Google Scholar 

  59. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  60. Ambroise C, McLachlan G (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci 99:6562–6566

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  61. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 58(1):267–288

    Google Scholar 

  62. Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K (2005) Sparsity and smoothness via the fused lasso. J R Stat Soc Series B Stat Methodol 67:91–108

    Article  Google Scholar 

  63. Bach F, Jenatton R, Mairal J, Obozinski G (2012) Structured sparsity through convex optimization. Stat Sci 27:450–468

    Article  Google Scholar 

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Acknowledgments

The authors thank Dr. Christian Jäger for the critical review of the book chapter.

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Correspondence to Karsten Hiller .

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Trezzi, JP., Vlassis, N., Hiller, K. (2015). The Role of Metabolomics in the Study of Cancer Biomarkers and in the Development of Diagnostic Tools. In: Scatena, R. (eds) Advances in Cancer Biomarkers. Advances in Experimental Medicine and Biology, vol 867. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7215-0_4

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