Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MS n ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.
References
[1]
Bentley, D.R. Genomic sequence information should be released immediately and freely in the public domain. Science 1996, 274, 533–534, doi:10.1126/science.274.5287.533.
[2]
Bentley, D.R. Genomes for medicine. Nature 2004, 429, 440–445, doi:10.1038/nature02622.
[3]
Kruglyak, L.; Nickerson, D.A. Variation is the spice of life. Nat. Genet. 2001, 27, 234–236, doi:10.1038/85776.
[4]
Fiehn, O.; Kopka, J.; Trethewey, R.N.; Willmitzer, L. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal. Chem. 2000, 72, 3573–3580, doi:10.1021/ac991142i.
[5]
Tanaka, N.; Tolstikov, V.; Weckwerth, W.; Fiehn, O.; Fukusaki, H. Micro HPLC for Metabolomics. In Frontier of Metabolomic Research; Springer-Verlag: Tokyo, Japan, 2003; pp. 85–100.
[6]
Ikegami, T.; Kobayashi, H.; Kimura, H.; Tolstikov, V.; Fiehn, O.; Tanaka, N. High-performance liquid chromatography for metabolomics: High-efficiency separations utilizing monolithic silica columns. In Metabolomics: The Frontier of Systems Biology; Springer-Verlag: Tokyo, 2005; pp. 107–126.
[7]
Tanaka, N.; Kimura, H.; Tokuda, D.; Hosoya, K.; Ikegami, T.; Ishizuka, N.; Minakuchi, H.; Nakanishi, K.; Shintani, Y.; Furuno, M; et al. Simple and comprehensive two-dimensional reversed-phase HPLC using monolithic silica columns. Anal. Chem. 2004, 76, 1273–1281, doi:10.1021/ac034925j.
[8]
Tanaka, N.; Kobayashi, H. Monolithic columns for liquid chromatography. Anal. Bioanal. Chem. 2003, 376, 298–301.
Tolstikov, V.V.; Fiehn, O.; Tanaka, N. Application of liquid chromatography-mass spectrometry analysis in metabolomics: reversed-phase monolithic capillary chromatography and hydrophilic chromatography coupled to electrospray ionization-mass spectrometry. In Metabolomics: Methods and Protocols (Methods in Molecular Biology); Weckwerth, W., Ed.; Humana Press: Totowa, NJ, USA, 2007; Volume 358, pp. 141–155.
[11]
Tolstikov, V.V.; Lommen, A.; Nakanishi, K.; Tanaka, N.; Fiehn, O. Monolithic silica-based capillary reversed-phase liquid chromatography/electrospray mass spectrometry for plant metabolomics. Anal. Chem. 2003, 75, 6737–6740, doi:10.1021/ac034716z.
[12]
Plumb, R.S.; Granger, J.H.; Stumpf, C.L.; Johnson, K.A.; Smith, B.W.; Gaulitz, S.; Wilson, I.D.; Castro-Perez, J. A rapid screening approach to metabonomics using UPLC and q-TOF mass spectrometry: Application to age, gender and diurnal variation in normal/Zucker obese rats and black, white and nude mice. Analyst 2005, 130, 844–849, doi:10.1039/b501767j.
[13]
Hemstrom, P.; Irgum, K. Hydrophilic interaction chromatography. J. Sep. Sci. 2006, 29, 1784–1821, doi:10.1002/jssc.200600199.
[14]
Takahashi, N. Three-dimensional mapping of N-linked oligosaccharides using anion-exchange, hydrophobic and hydrophilic interaction modes of high-performance liquid chromatography. J. Chromatogr. A 1996, 720, 217–225, doi:10.1016/0021-9673(95)00328-2.
[15]
Tolstikov, V.V.; Fiehn, O. Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal. Biochem. 2002, 301, 298–307, doi:10.1006/abio.2001.5513.
[16]
Alpert, A.J. Electrostatic repulsion hydrophilic interaction chromatography for isocratic separation of charged solutes and selective isolation of phosphopeptides. Anal. Chem. 2008, 80, 62–76, doi:10.1021/ac070997p.
[17]
Mizzen, C.A.; Alpert, A.J.; Levesque, L.; Kruck, T.P.; McLachlan, D.R. Resolution of allelic and non-allelic variants of histone H1 by cation-exchange-hydrophilic-interaction chromatography. J. Chromatogr. B Biomed. Sci. Appl. 2000, 744, 33–46, doi:10.1016/S0378-4347(00)00210-3.
[18]
Alpert, A.J.; Shukla, M.; Shukla, A.K.; Zieske, L.R.; Yuen, S.W.; Ferguson, M.A.; Mehlert, A.; Pauly, M.; Orlando, R. Hydrophilic-interaction chromatography of complex carbohydrates. J. Chromatogr. A 1994, 676, 191–122, doi:10.1016/0021-9673(94)00467-6.
[19]
Boutin, J.A.; Ernould, A.P.; Ferry, G.; Genton, A.; Alpert, A.J. Use of hydrophilic interaction chromatography for the study of tyrosine protein kinase specificity. J. Chromatogr. 1992, 583, 137–143.
[20]
Alpert, A.J. Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. J. Chromatogr. 1990, 499, 177–196, doi:10.1016/S0021-9673(00)96972-3.
[21]
Fiehn, O. Metabolite profiling in Arabidopsis. Arabidopsis Protoc. Methods Mol. Biol. 2006, 323, 439–447.
Dietmair, S.; Timmins, N.E.; Gray, P.P.; Nielsen, L.K.; Kr？mer, J.O. Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol. Anal. Biochem. 2010, 404, 155–164, doi:10.1016/j.ab.2010.04.031.
[24]
Zou, W.; Tolstikov, V.V. Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach. Rapid Commun. Mass Spectrom. 2008, 22, 1312–1324, doi:10.1002/rcm.3507.
[25]
Zou, W.; Tolstikov, V.V. Pattern recognition and pathway analysis with genetic algorithms in mass spectrometry based metabolomics. Algorithms 2009, 2, 638–666, doi:10.3390/a2020638.
[26]
Scholz, M.; Fiehn, O. SetupX—A public study design database for metabolomic projects. Pac. Symp. Biocomput. 2007, 12, 169–180.
[27]
Fiehn, O.; Wohlgemuth, G.; Scholz, M. Setup and Annotation of Metabolomic Experiments by Integrating Biological and Mass Spectrometric Metadata. Data Integr. Life Sci. 2005, 3615, 224–239.
[28]
Fiehn, O.; Wohlgemuth, G.; Scholz, M.; Kind, T.; Lee, D.Y.; Lu, Y.; Moon, S.; Nikolau, B. Quality control for plant metabolomics: Reporting MSI-compliant studies. Plant J. 2008, 53, 691–704, doi:10.1111/j.1365-313X.2007.03387.x.
[29]
Shulaev, V. Metabolomics technology and bioinformatics. Brief. Bioinform. 2006, 7, 128–139, doi:10.1093/bib/bbl012.
[30]
Jain, A.K.; Duin, R.P.W.; Mao, J. Statistical pattern recognition: A review. Trans. Pattern Anal. Machine Intell. 2000, 22, 4–37, doi:10.1109/34.824819.
[31]
MetaboAnalyst. Available online: http://www.metaboanalyst.ca/MetaboAnalyst/faces/Home.jsp/ (accessed on 9 June 2013).
[32]
Scholz, M.; Gatzek, S.; Sterling, A.; Fiehn, O.; Selbig, J. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 2004, 20, 2447–2454, doi:10.1093/bioinformatics/bth270.
Goodacre, R.; York, E.V.; Heald, J.K.; Scott, I.M. Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry. Phytochemistry 2003, 62, 859–863, doi:10.1016/S0031-9422(02)00718-5.
[36]
Saeys, Y.; Inza, I.; Larranaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23, 2507–2517, doi:10.1093/bioinformatics/btm344.
[37]
Lee, J.W.; Lee, J.B.; Park, M.; Song, S.H. An extensive comparison of recent classification tools applied to microarray data. Comput. Stat. Data Anal. 2005, 48, 869–885, doi:10.1016/j.csda.2004.03.017.
[38]
Zhang, X.; Lu, X.; Shi, Q.; Xu, X.Q.; Leung, H.C.; Harris, L.; Iglehart, J.; Miron, A.; Liu, J.; Wong, W. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 2006, 7, 197, doi:10.1186/1471-2105-7-197.
[39]
Goodacre, R. Making sense of the metabolome using evolutionary computation: Seeing the wood with the trees. J. Exp. Bot. 2005, 56, 245–254, doi:10.1093/jxb/eri043.
[40]
Trevino, V.; Falciani, F. GALGO: An R package for multivariate variable selection using genetic algorithms. Bioinformatics 2006, 22, 1154–1156, doi:10.1093/bioinformatics/btl074.
[41]
Jeffries, N.O. Performance of a genetic algorithm for mass spectrometry proteomics. BMC Bioinf. 2004, doi:10.1186/1471-2105-5-180.
[42]
Shulaev, V. Metabolic fingerprinting of breast cancer development. In Proceedings of Biomarker Discovery Summit, Philadelphia, PA, USA, 29 September–1 October, 2008.
[43]
Zou, W.; Yasuor, H.; Fischer, A.J.; Tolstikov, V.V. Trace metabolic profiling and pathway analysis of clomazone using LC-MS-MS and high-resolution MS. LCGC 2011, 29, 760–769.
[44]
Metlin. Available online: http://metlin.scripps.edu/ (accessed on 9 June 2013).
[45]
MassBank. Available online: http://www.massbank.jp/ (accessed on 9 June 2013).
[46]
Human Metabolome Database. Available online: http://www.hmdb.ca/ (accessed on 9 June 2013).
[47]
Lipid Maps. Available online: http://www.lipidmaps.org/ (accessed on 9 June 2013).
[48]
Binbase. Available online: http://fiehnlab.ucdavis.edu/projects/binbase_setupx/ (accessed on 9 June 2013).
[49]
Kyoto Encyclopedia of Genes and Genomes. Available online: http://www.genome.jp/kegg/ (accessed on 9 June 2013).
[50]
Kind, T.; Fiehn, O. Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinf. 2006, 7, 234, doi:10.1186/1471-2105-7-234.
[51]
Kind, T.; Fiehn, O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinf. 2007, 8, 105, doi:10.1186/1471-2105-8-105.
[52]
Zou, W.; Wang, Y.D.; Gu, M.; Tolstikov, V. Optimization of mass accuracy, spectral accuracy, and resolution in metabolite identification using LTQ-FT Ultra hybrid mass spectrometer. In Proceedings of 57th ASMS Conference on Mass Spectrometry and Allied Topics, Philadelphia, PA, USA, June 2009.
[53]
PubChem. Available online: http://pubchem.ncbi.nlm.nih.gov/ (accessed on 9 June 2013).
[54]
Chemical Structure Lookup Service. Available online: http://cactus.nci.nih.gov/cgi-bin/lookup/search/ (accessed on 9 June 2013).
[55]
CHEMnetBASE. Available online: http://dnp.chemnetbase.com/ (accessed on 9 June 2013).
[56]
ChemSpider. Available online: http://chemspider.com/ (accessed on 9 June 2013).
[57]
Serkova, N.J.; Brown, M.S. Quantitative analysis in magnetic resonance spectroscopy: From metabolic profiling to in vivo biomarkers. Bioanalysis 2012, 4, 321–341, doi:10.4155/bio.11.320.
[58]
Serkova, N.J.; Glunde, K. Metabolomics of cancer. Methods Mol Biol. 2009, 520, 273–295, doi:10.1007/978-1-60327-811-9_20.
[59]
Zou, W.; Tolstikov, V. Predictive multiple reactions monitoring (pMRM) in metabolomics. In Proceedings of 5th Annual Metabolomics Society International Conference, Edmonton, Alberta, Canada, August 2009.
[60]
Yasuor, H.; Zou, W.; Tolstikov, V.; Tjeerdema, R.; Fischer, A. Differential oxidative metabolism and 5-ketoclomazone accumulation are involved in Echinochloa phyllopogon resistance to clomazone. Plant Physiol. 2010, 153, 319–326, doi:10.1104/pp.110.153296.
[61]
Tomco, P.; Holstege, D.; Zou, W.; Tjeerdema, R. Microbial deg radation of clomazone under simulated california rice field conditions. J. Agric. Food Chem. 2010, 58, 3674–3680, doi:10.1021/jf903957j.
[62]
Duan, Y.Y.; Ma, X.C.; Zou, W.; Wang, C.; Saramipoor, I.; Ahuja, T.; Tolstikov, V.; Zern, M.A. Differentiation and characterization of metabolically functioning hepatocytes from human embryonic stem cells. Stem Cells 2010, 28, 674–686, doi:10.1002/stem.315.
[63]
Ma, S.; Zhu, M. Recent advances in applications of liquid chromatography-tandem mass spectrometry to the analysis of reactive drug metabolites. Chem. Biol. Interact. 2009, 179, 25–37, doi:10.1016/j.cbi.2008.09.014.
[64]
Ma, S.; Subramanian, R. Detecting and characterizing reactive metabolites by liquid chromatography/tandem mass spectrometry. J. Mass Spectrom. 2006, 41, 1121–1139, doi:10.1002/jms.1098.
[65]
Yao, M.; Ma, L.; Duchoslav, E.; Zhu, M. Rapid screening and characterization of drug metabolites using multiple ion monitoring dependent product ion scan and postacquisition data mining on a hybrid triple quadrupole-linear ion trap mass spectrometer. Rapid Commun. Mass Spectrom. 2009, 23, 1683–1693, doi:10.1002/rcm.4045.
[66]
Li, A.C.; Gohdes, M.A.; Shou, W.Z. “N-in-one” strategy for metabolite identification using a liquid chromatography/hybrid triple quadrupole linear ion trap instrument using multiple dependent product ion scans triggered with full mass scan. Rapid Commun. Mass Spectrom. 2007, 21, 1421–1430, doi:10.1002/rcm.2976.
[67]
Gao, H.; Materne, O.L.; Howe, D.L.; Brummel, C.L. Method for rapid metabolite profiling of drug candidates in fresh hepatocytes using liquid chromatography coupled with a hybrid quadrupole linear ion trap. Rapid Commun. Mass Spectrom. 2007, 21, 3683–3693, doi:10.1002/rcm.3257.
[68]
Holcapek, M.; Kolarova, L.; Nobilis, M. High-performance liquid chromatography-tandem mass spectrometry in the identification and determination of phase I and phase II drug metabolites. Anal. Bioanal. Chem. 2008, 391, 59–78, doi:10.1007/s00216-008-1962-7.
[69]
Fiehn, O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp.Funct.Genomics 2001, 2, 155–168, doi:10.1002/cfg.82.
[70]
MetPA. Available online: http://metpa.metabolomics.ca/MetPA/ (accessed on 9 June 2013).
[71]
Ingenuity Pathway Analysis Systems. Available online: http://www.ingenuity.com/ (accessed on 9 June 2013).
[72]
Cline, M.S.; Smoot, M.; Cerami, E.; Kuchinsky, A.; Landys, N.; Workman, C.; Christmas, R.; Avila-Campilo, I.; Creech, B; Gross, B.; et al. Integration of biological networks and gene expression data using cytoscape. Nat. Protocols. 2007, 2, 2366–2382, doi:10.1038/nprot.2007.324.
[73]
Urayama, S.; Zou, W.; Brooks, K.; Tolstikov, V.V. Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. Rapid Commun. Mass Spectrom. 2010, 24, 613–620, doi:10.1002/rcm.4420.
[74]
Hruban, R.H.; Klein, A.P.; Eshleman, J.R.; Axilbund, J.E.; Goggins, M. Familial pancreatic cancer: from genes to improved patient care. Exp. Rev. Gastroenterol. Hepatol. 2007, 1, 81–88, doi:10.1586/17474124.1.1.81.
[75]
Abbruzzese, J.L. The challenge of pancreatic cancer. Int. J. Gastrointest Cancer 2003, 33, 1–2, doi:10.1385/IJGC:33:1:1.
Bardeesy, N.; DePinho, R.A. Pancreatic cancer biology and genetics. Nat. Rev. Cancer 2002, 2, 897–909, doi:10.1038/nrc949.
[78]
Griffin, J.F.; Smalley, S.R.; Jewell, W.; Paradelo, J.C.; Reymond, R.D.; Hassanein, R.E.; Evans, R.G. Patterns of failure after curative resection of pancreatic carcinoma. Cancer 1990, 66, 56–61, doi:10.1002/1097-0142(19900701)66:1<56::AID-CNCR2820660112>3.0.CO;2-6.
[79]
MSConvert. Available online: http://proteowizard.sourceforge.net/tools/msconvert.html/ (accessed on 9 June 2013).
[80]
Statistica. Available online: http://www.statsoft.com/ (accessed on 9 June 2013).
[81]
Tolstikov, V.V. Metabolic biomarkers discovery project. In Proceedings of ACS National Meeting & Exposition, Anaheim, CA, USA; 2011.
[82]
Tolstikov, V.V. Metabolic biomarkers discovery project (MBDP). In Proceedings of Pancreatic Cancer Diagnostic Test Development, Molecular Diagnostics World Congress, South San Francisco, CA, USA; 2011.
[83]
Tolstikov, V. Mass spectrometry-derived metabolic biomarkers and signatures in diagnostic development. In Proceedings of Biomarker Discovery Summit, Philadelphia, PA, USA, 29 September–1 October 2008.
[84]
Kemsley, E.K.; Le Gall, G.; Dainty, J.R.; Watson, A.D.; Harvey, L.J.; Tapp, H.S.; Colquhoun, I.J. Multivariate techniques and their application in nutrition: A metabolomics case study. Br. J. Nutr. 2007, 98, 1–14, doi:10.1017/S0007114507685365.
