Skip to main content

Advertisement

SpringerLink
Log in

Approaches for the study of cancer: towards the integration of genomics, proteomics and metabolomics

  • Educational Series/Red Series
  • Current Technology in Cancer Research and Treatment
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Recent technological advances, combined with the development of bioinformatic tools, allow us to better address biological questions combining -omic approaches (i.e., genomics, metabolomics and proteomics). This novel comprehensive perspective addresses the identification, characterisation and quantitation of the whole repertoire of genes, proteins and metabolites occurring in living organisms. Here we provide an overview of recent significant advances and technologies used in genomics, metabolomics and proteomics. We also underline the importance and limits of mass accuracy in mass spectrometry-based -omics and briefly describe emerging types of fragmentation used in mass spectrometry. The range of instruments and techniques used to address the study of each -omic approach, which provide vast amounts of information (usually termed ‘high-throughput’ technologies in the literature) is briefly discussed, including names, links and descriptions of the main databases, data repositories and resources used. Integration of multiple -omic results and procedures seems necessary. Therefore, an emerging challenge is the integration of the huge amount of data generated and the standardisation of the procedures and methods used. Functional data integration will lead to answers to unsolved questions, hopefully, applicable to clinical practice and management of patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Casado-Vela J, Cebrian A, del Pulgar MT et al (2011) Lights and shadows of proteomic technologies for the study of protein species including isoforms, splicing variants and protein post-translational modifications. Proteomics 11:590–603

    Article  PubMed  CAS  Google Scholar 

  2. Casado-Vela J, Del Pulgar TG, Cebrian A et al (2011) Human urine proteomics: building a list of human urine cancer biomarkers. Expert Rev Proteomics 8:347–360

    Article  PubMed  CAS  Google Scholar 

  3. Schuler GD, Boguski MS, Stewart EA et al (1996) A gene map of the human genome. Science 274:540–546

    Article  PubMed  CAS  Google Scholar 

  4. Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921

    Article  PubMed  CAS  Google Scholar 

  5. Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351

    Article  PubMed  CAS  Google Scholar 

  6. James P (1997) Protein identification in the postgenome era: the rapid rise of proteomics. Q Rev Biophys 30:279–331

    Article  PubMed  CAS  Google Scholar 

  7. Sastre L (2011) New DNA sequencing technologies open a promising era for cancer research and treatment. Clin Transl Oncol 13:301–306

    Article  PubMed  Google Scholar 

  8. Hobert O (2010) The impact of whole genome sequencing on model system genetics: get ready for the ride. Genetics 184:317–319

    Article  PubMed  CAS  Google Scholar 

  9. Campbell PJ, Stephens PJ, Pleasance ED et al (2008) Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat Genet 40:722–729

    Article  PubMed  CAS  Google Scholar 

  10. Ley TJ, Mardis ER, Ding L et al (2008) DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456:66–72

    Article  PubMed  CAS  Google Scholar 

  11. Puente XS, Pinyol M, Quesada V et al (2011) Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 475:101–105

    Article  PubMed  CAS  Google Scholar 

  12. He L, Hannon GJ (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5:522–531

    Article  PubMed  CAS  Google Scholar 

  13. Holliday R (1987) The inheritance of epigenetic defects. Science 238:163–170

    Article  PubMed  CAS  Google Scholar 

  14. Jones PA, Baylin SB (2002) The fundamental role of epigenetic events in cancer. Nat Rev Genet 3:415–428

    Article  PubMed  CAS  Google Scholar 

  15. Esteller M (2008) Epigenetics in cancer. N Engl J Med 358:1148–1159

    Article  PubMed  CAS  Google Scholar 

  16. Gopalakrishnan S, Van Emburgh BO, Robertson KD (2008) DNA methylation in development and human disease. Mutat Res 647:30–38

    PubMed  CAS  Google Scholar 

  17. Martin-Subero JI, Esteller M (2011) Profiling epigenetic alterations in disease. Adv Exp Med Biol 711:162–177

    Article  PubMed  Google Scholar 

  18. Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455:1054–1056

    Article  PubMed  CAS  Google Scholar 

  19. Wishart DS (2007) Proteomics and the human metabolome project. Expert Rev Proteomics 4:333–335

    Article  PubMed  CAS  Google Scholar 

  20. Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–610

    Article  PubMed  CAS  Google Scholar 

  21. Ramirez de Molina A, Banez-Coronel M, Gutierrez R et al (2004) Choline kinase activation is a critical requirement for the proliferation of primary human mammary epithelial cells and breast tumor progression. Cancer Res 64:6732–6739

    Article  PubMed  Google Scholar 

  22. Hernando E, Sarmentero-Estrada J, Koppie T et al (2009) A critical role for choline kinase-alpha in the aggressiveness of bladder carcinomas. Oncogene 28:2425–2435

    Article  PubMed  CAS  Google Scholar 

  23. Schiller J, Arnold K (2002) Application of high resolution 31P NMR spectroscopy to the characterization of the phospholipid composition of tissues and body fluids: a methodological review. Med Sci Monit 8:MT205–222

    PubMed  CAS  Google Scholar 

  24. Wilkins MR, Sanchez JC, Gooley AA et al (1996) Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev 13:19–50

    PubMed  CAS  Google Scholar 

  25. Casado J, Casal JI (2008) Proteomics. In: Gad SC (ed.) Preclinical development handbook: toxicology. John Wiley & Sons, New Jersey, pp 839–865

    Chapter  Google Scholar 

  26. Ong SE, Mann M (2005) Mass spectrometrybased proteomics turns quantitative. Nat Chem Biol 1:252–262

    Article  PubMed  CAS  Google Scholar 

  27. Rabilloud T (2002) Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics 2:3–10

    Article  PubMed  CAS  Google Scholar 

  28. Celis JE, Gromov P, Ostergaard M et al (1996) Human 2-D PAGE databases for proteome analysis in health and disease: http://biobase.dk/cgi-bin/celis. FEBS Lett 398:129–134

    Article  PubMed  CAS  Google Scholar 

  29. Mann M, Jensen ON (2003) Proteomic analysis of post-translational modifications. Nat Biotechnol 21:255–261

    Article  PubMed  CAS  Google Scholar 

  30. Jensen ON (2004) Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr Opin Chem Biol 8:33–41

    Article  PubMed  Google Scholar 

  31. Bruckner A, Polge C, Lentze N et al (2009) Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci 10:2763–2788

    Article  PubMed  CAS  Google Scholar 

  32. Xu X, Song Y, Li Y et al (2010) The tandem affinity purification method: an efficient system for protein complex purification and protein interaction identification. Protein Expr Purif 72:149–156

    Article  PubMed  CAS  Google Scholar 

  33. Sibani S, LaBaer J (2011) Immunoprofiling using NAPPA protein microarrays. Methods Mol Biol 723:149–161

    Article  PubMed  CAS  Google Scholar 

  34. He M, Stoevesandt O, Taussig MJ (2008) In situ synthesis of protein arrays. Curr Opin Biotechnol 19:4–9

    Article  PubMed  CAS  Google Scholar 

  35. Braun P, Tasan M, Dreze M et al (2009) An experimentally derived confidence score for binary protein-protein interactions. Nat Methods 6:91–97

    Article  PubMed  CAS  Google Scholar 

  36. Chen J, Zhou J, Sanders CK et al (2009) A surface display yeast two-hybrid screening system for high-throughput protein interactome mapping. Anal Biochem 390:29–37

    Article  PubMed  CAS  Google Scholar 

  37. Chen Q, Chen J, Sun T et al (2004) A yeast two-hybrid technology-based system for the discovery of PPARgamma agonist and antagonist. Anal Biochem 335:253–259

    Article  PubMed  CAS  Google Scholar 

  38. Chen YC, Rajagopala SV, Stellberger T et al (2010) Exhaustive benchmarking of the yeast two-hybrid system. Nat Methods 7:667–668; author reply 668

    Article  PubMed  CAS  Google Scholar 

  39. Wong RL, Amster IJ (2006) Sub part-per-million mass accuracy by using stepwise-external calibration in Fourier transform ion cyclotron resonance mass spectrometry. J Am Soc Mass Spectrom 17:1681–1691

    Article  PubMed  CAS  Google Scholar 

  40. O'Hagan S, Dunn WB, Knowles JD et al (2007) Closed-loop, multiobjective optimization of twodimensional gas chromatography/mass spectrometry for serum metabolomics. Anal Chem 79:464–476

    Article  PubMed  Google Scholar 

  41. Wenger CD, McAlister GC, Xia Q et al (2010) Sub-part-per-million precursor and product mass accuracy for high-throughput proteomics on an electron transfer dissociation-enabled orbitrap mass spectrometer. Mol Cell Proteomics 9:754–763

    Article  PubMed  CAS  Google Scholar 

  42. Martinez Ballesta MD, Casado-Vela J, Muries B et al (2010) Analysis of root plasma membrane aquaporins from Brassica oleracea: post-translational modifications, de novo sequencing and detection of isoforms by high resolution mass spectrometry. J Proteome Res 9:3479–3494

    Article  PubMed  Google Scholar 

  43. Olsen JV, Macek B, Lange O et al (2007) Higherenergy C-trap dissociation for peptide modification analysis. Nat Methods 4:709–712

    Article  PubMed  CAS  Google Scholar 

  44. Horn DM, Zubarev RA, McLafferty FW (2000) Automated de novo sequencing of proteins by tandem high-resolution mass spectrometry. Proc Natl Acad Sci U S A 97:10313–10317

    Article  PubMed  CAS  Google Scholar 

  45. Syka JE, Coon JJ, Schroeder MJ et al (2004) Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc Natl Acad Sci U S A 101:9528–9533

    Article  PubMed  CAS  Google Scholar 

  46. Casado-Vela J, Martinez-Esteso MJ, Rodriguez E et al (2010) iTRAQ-based quantitative analysis of protein mixtures with large fold change and dynamic range. Proteomics 10:343–347

    Article  PubMed  CAS  Google Scholar 

  47. Lopez-Llorca LV, Gomez-Vidal S, Monfort E et al (2010) Expression of serine proteases in eggparasitic nematophagous fungi during barley root colonization. Fungal Genet Biol 47:342–351

    Article  PubMed  CAS  Google Scholar 

  48. Casado-Vela J, Muries B, Carvajal M et al (2010) Analysis of root plasma membrane aquaporins from Brassica oleracea: post-translational modifications, de novo sequencing and detection of isoforms by high resolution mass spectrometry. J Proteome Res 9:3479–3494

    Article  PubMed  CAS  Google Scholar 

  49. Field D, Garrity G, Gray T et al (2008) The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 26:541–547

    Article  PubMed  CAS  Google Scholar 

  50. Brazma A, Hingamp P, Quackenbush J et al (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29:365–371

    Article  PubMed  CAS  Google Scholar 

  51. Taylor CF, Paton NW, Lilley KS et al (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25:887–893

    Article  PubMed  CAS  Google Scholar 

  52. Lonjon N, Prieto M, Haton H et al (2009) Minimum information about animal experiments: supplier is also important. J Neurosci Res 87:403–407

    Article  PubMed  CAS  Google Scholar 

  53. Le Novere N, Finney A, Hucka M et al (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515

    Article  PubMed  Google Scholar 

  54. Orchard S, Salwinski L, Kerrien S et al (2007) The minimum information required for reporting a molecular interaction experiment (MIMIx). Nat Biotechnol 25:894–898

    Article  PubMed  CAS  Google Scholar 

  55. Deutsch EW, Ball CA, Berman JJ et al (2008) Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). Nat Biotechnol 26:305–312

    Article  PubMed  CAS  Google Scholar 

  56. Ejsing CS, Sampaio JL, Surendranath V et al (2009) Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci U S A 106:2136–2141

    Article  PubMed  CAS  Google Scholar 

  57. Picotti P, Bodenmiller B, Mueller LN et al (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795–806

    Article  PubMed  CAS  Google Scholar 

  58. Picotti P, Lam H, Campbell D et al (2008) A database of mass spectrometric assays for the yeast proteome. Nat. Methods 5:913–914

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Translational Oncology Unit, Instituto de Investigaciones Biomédicas “Alberto Sols” Spanish National Research Council (CSIC-UAM), C/Arturo Duperier, 4, ES-28029, Madrid, Spain

    Juan Casado-Vela, Arancha Cebrián, María Teresa Gómez del Pulgar & Juan Carlos Lacal

Authors
  1. Juan Casado-Vela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arancha Cebrián
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María Teresa Gómez del Pulgar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Carlos Lacal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Carlos Lacal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Casado-Vela, J., Cebrián, A., Gómez del Pulgar, M.T. et al. Approaches for the study of cancer: towards the integration of genomics, proteomics and metabolomics. Clin Transl Oncol 13, 617–628 (2011). https://doi.org/10.1007/s12094-011-0707-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-011-0707-9

Keywords

Search

Navigation