Experimental and Study Design Considerations for Uncovering Oncometabolites

  • Majda Haznadar
  • Ewy A. MathéEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1513)


Metabolomics as a field has gained attention due to its potential for biomarker discovery, namely because it directly reflects disease phenotype and is the downstream effect of posttranslational modifications. The field provides a “top-down,” integrated view of biochemistry in complex organisms, as opposed to the traditional “bottom-up” approach that aims to analyze networks of interactions between genes, proteins and metabolites. It also allows for the detection of thousands of endogenous metabolites in various clinical biospecimens in a high-throughput manner, including tissue and biofluids such as blood and urine. Of note, because biological fluid samples can be collected relatively easily, the time-dependent fluctuations of metabolites can be readily studied in detail.

In this chapter, we aim to provide an overview of (1) analytical methods that are currently employed in the field, and (2) study design concepts that should be considered prior to conducting high-throughput metabolomics studies. While widely applicable, the concepts presented here are namely applicable to high-throughput untargeted studies that aim to search for metabolite biomarkers that are associated with a particular human disease.

Key words

Metabolomics Biomarker discovery Study design Mass spectrometry Oncometabolites Analytical techniques 


  1. 1.
    Dunn WB, Broadhurst DI, Atherton HJ et al (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426CrossRefPubMedGoogle Scholar
  2. 2.
    van der Greef J, Stroobant P, van der Heijden R (2004) The role of analytical sciences in medical systems biology. Curr Opin Chem Biol 8:559–565CrossRefPubMedGoogle Scholar
  3. 3.
    Stepien M, Duarte-Salles T, Fedirko V et al (2016) Alteration of amino acid and biogenic amine metabolism in hepatobiliary cancers: findings from a prospective cohort study. Int J Can 138:348–360CrossRefGoogle Scholar
  4. 4.
    Dang L, White DW, Gross S et al (2010) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 465:966CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ward PS, Patel J, Wise DR et al (2010) The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 17:225–234CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Pollard PJ, Briere JJ, Alam NA et al (2005) Accumulation of Krebs cycle intermediates and over-expression of HIF1alpha in tumours which result from germline FH and SDH mutations. Hum Mol Genet 14:2231–2239CrossRefPubMedGoogle Scholar
  7. 7.
    Robaglia A, Cau P, Bottini J, Seite R (1989) Effects of isolation and high helium pressure on the nucleolus of sympathetic neurons in the rat superior cervical ganglion. J Auton Neurosci 27:207–219Google Scholar
  8. 8.
    Yin P, Peter A, Franken H et al (2013) Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin Chem 59:833–845CrossRefPubMedGoogle Scholar
  9. 9.
    Libiseller G, Dvorzak M, Kleb U et al (2015) IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16:118CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Clasquin MF, Melamud E, Rabinowitz JD (2012) LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinform. Chapter 14:Unit14. 11. doi: 10.1002/0471250953.bi1411s37.
  11. 11.
    Smith CA, O'Maille G, Want EJ et al (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27:747–751CrossRefPubMedGoogle Scholar
  12. 12.
    Wishart DS, Jewison T, Guo AC et al (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41:D801–D807CrossRefPubMedGoogle Scholar
  13. 13.
    Skogerson K, Wohlgemuth G, Barupal DK, Fiehn O (2011) The volatile compound BinBase mass spectral database. BMC Bioinformatics 12:321CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Haug K, Salek RM, Conesa P et al (2013) MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786CrossRefPubMedGoogle Scholar
  15. 15.
    Beynon RJ, Pratt JM (2005) Metabolic labeling of proteins for proteomics. Mol Cell Proteomics 4:857–872CrossRefPubMedGoogle Scholar
  16. 16.
    Rousseaux M, Petit H, Hache JC et al (1985) Ocular and head movements in infarctions of the thalamic region. Rev Neurol 141:391–403PubMedGoogle Scholar
  17. 17.
    Fung ET, Enderwick C (2002) ProteinChip clinical proteomics: computational challenges and solutions. Biotechniques Suppl:34–38, 40–41PubMedGoogle Scholar
  18. 18.
    Warrack BM, Hnatyshyn S, Ott KH et al (2009) Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci 877:547–552CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Laboratory of Human Carcinogenesis, Center for Cancer ResearchNational Cancer InstituteBethesdaUSA
  2. 2.Biomedical Informatics DepartmentCollege of Medicine, Ohio State UniversityColumbusUSA

Personalised recommendations