Skip to main content
Log in

La métabolomique: un nouvel outil pour la recherche translationnelle en cancérologie

Metabolomics: a novel tool for translational research in oncology

  • Synthèse / Review Article
  • Published:
Oncologie

Abstract

Development and validation of novel diagnostic and prognostic tools for cancer patients are a clinically unmet need, especially to help predicting survival or treatment response and toxicity. Metabolomics provides a dynamic portrait of the metabolic state of a tumour in response to pathophysiological stimuli (such as tumour growth or tumour shrinkage) and helps us understand the molecular mechanism sustaining these phenomena. Recent literature presented encouraging data on potential applications of metabolomics in translational research. Analysis of alterations of the metabolic network may lead to identify novel biomarkers and/or therapeutic target.

Résumé

La prise en charge des patients atteints de cancer nécessite le développement et la validation de nouveaux outils diagnostiques, pronostiques et/ou prédictifs de la réponse ou de la toxicité des traitements. La métabolomique dresse un portrait dynamique de l’état métabolique d’une tumeur, en réponse à des stimuli physiopathologiques (prolifération ou fonte tumorale), permettant la compréhension des mécanismes moléculaires impliqués. Les premières études publiées présentent des données encourageantes pour la poursuite de l’utilisation de lamétabolomique dans la recherche de transfert en cancérologie. L’étude des perturbations du réseau métabolique pourrait permettre en effet d’identifier de nouveaux biomarqueurs et/ou de nouvelles cibles thérapeutiques.

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

Access this article

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

Références

  1. Bathen TF, Jensen LR, Sitter B, et al. (2007) MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat 104: 181–189

    Article  PubMed  Google Scholar 

  2. Benyoseph O, Badargoffer RS, Morris PG, et al. (1993) Glycerol 3-phosphate and lactate as indicators of the cerebral cytoplasmic redox state in severe and mild hypxia respectively: a C-13-NMR and P-31-NMR study. Biochem J 291: 915–919

    CAS  Google Scholar 

  3. Cairns RA, Papandreou I, Sutphin PD, et al. (2007) Metabolic targeting of hypoxia and HIF1 in solid tumors can enhance cytotoxic chemotherapy. Proc Natl Acad Sci USA 104: 9445–9450

    Article  CAS  PubMed  Google Scholar 

  4. Chan EC, Koh PK, Mal M, et al. (2009) 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 Proteome Res 8: 352–3561

    Article  CAS  PubMed  Google Scholar 

  5. Cheng LL, Chang IW, Louis DN, et al. (1998) Correlation of high-resolution magic angle spinning proton magnetic resonance spectroscopy with histopathology of intact human brain tumor specimens. Cancer Res 58: 1825–1832

    CAS  PubMed  Google Scholar 

  6. Cheng LL, Ma MJ, Becerra L, et al. (1997) Quantitative neuropathology by high resolution magic angle spinning proton magnetic resonance spectroscopy. Proc Natl Acad Sci USA 94: 6408–6413

    Article  CAS  PubMed  Google Scholar 

  7. Clayton TA, Lindon JC, Cloarec O, et al. (2006) Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440: 1073–1077

    Article  CAS  PubMed  Google Scholar 

  8. Coy JF, Dressler D, Wilde J, et al. (2005) Mutations in the transketolase-like gene TKTL1: clinical implications for neurodegenerative diseases, diabetes and cancer. Clin Lab 51: 257–273

    CAS  PubMed  Google Scholar 

  9. Cui Q, Lewis IA, Hegeman AD, et al. (2008) Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26: 162–164

    Article  CAS  PubMed  Google Scholar 

  10. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26: 51–78

    Article  CAS  PubMed  Google Scholar 

  11. Dunn WB, Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. TrAC, Trends Anal Chem 24: 285–294

    Article  CAS  Google Scholar 

  12. Eisen MB, Spellman PT, Brown PO, et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863–4868

    Article  CAS  PubMed  Google Scholar 

  13. El-Deredy W, Ashmore SM, Branston NM, et al. (1997) Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks. Cancer Res 57: 4196–4199

    CAS  PubMed  Google Scholar 

  14. Ernst RR, Bodenhausen G, Wokaun A (1987) Principles of nuclear magnetic resonance in one and two dimensions. Clarendon Press, Oxford

    Google Scholar 

  15. Fiehn O, Kopka J, Dormann P, et al. (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18: 1157–1161

    Article  CAS  PubMed  Google Scholar 

  16. Glunde K, Serkova NJ (2006) Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism. Pharmacogenomics 7: 1109–1123

    Article  CAS  PubMed  Google Scholar 

  17. Griffin JL, Shockcor JP (2004) Metabolic profiles of cancer cells. Nat Rev Cancer 4: 551–561

    Article  CAS  PubMed  Google Scholar 

  18. Holmes E, Loo RL, Stamler J, et al. (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453: 396–400

    Article  CAS  PubMed  Google Scholar 

  19. Howe FA, Barton SJ, Cudlip SA, et al. (2003) Metabolic profiles of human brain tumors using quantitative in vivo H-1 magnetic resonance spectroscopy. Magn Reson Med 49: 223–232

    Article  CAS  PubMed  Google Scholar 

  20. Kroemer G, Pouyssegur J (2008) Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell 13: 472–482

    Article  CAS  PubMed  Google Scholar 

  21. Lindon JC, Holmes E, Nicholson JK (2003) So whats the deal with metabonomics? Metabonomics measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins. Anal Chem 75: 384A–391A

    Article  CAS  PubMed  Google Scholar 

  22. Lyng H, Sitter B, Bathen TF, et al. (2007) Metabolic mapping by use of high-resolution magic angle spinning H-1 MR spectroscopy for assessment of apoptosis in cervical carcinomas. BMC Cancer 7: 11

    Article  PubMed  Google Scholar 

  23. Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics: a review in human disease diagnosis. Anal Chim Acta 659: 23–33

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. Odunsi K, Wollman RM, Ambrosone CB, et al. (2005) Detection of epithelial ovarian cancer using H-1-NMR-based metabonomics. Int J Cancer 113: 782–788

    Article  CAS  PubMed  Google Scholar 

  26. Piotto M, Moussallieh FM, Dillmann B, et al. (2009) Metabolic characterization of primary human colorectal cancers using high resolution magic angle spinning H-1 magnetic resonance spectroscopy. Metabolomics 5: 292–301

    Article  CAS  Google Scholar 

  27. Powers R (2009) NMR metabolomics and drug discovery. Magn Reson Chem 47: S2–S11

    CAS  PubMed  Google Scholar 

  28. Preul MC, Caramanos Z, Collins DL, et al. (1996) Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy. Nat Med 2: 323–325

    Article  CAS  PubMed  Google Scholar 

  29. Robertson DG (2005) Metabonomics in toxicology: a review. Toxicol Sci 85: 809–822

    Article  CAS  PubMed  Google Scholar 

  30. Shulaev V (2006) Metabolomics technology and bioinformatics. Brief Bioinform 7: 128–139

    Article  CAS  PubMed  Google Scholar 

  31. Spratlin JL, Serkova NJ, Eckhardt SG (2009) Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 15: 431–440

    Article  CAS  PubMed  Google Scholar 

  32. Sreekumar A, Poisson LM, Rajendiran TM, et al. (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457: 910–914

    Article  CAS  PubMed  Google Scholar 

  33. Stahle L, Wold S (1989) Analysis of variance (Anova). Chemometrics Intellig Lab Syst 6: 259–272

    Article  Google Scholar 

  34. Swanson MG, Zektzer AS, Tabatabai ZL, et al. (2006) Quantitative analysis of prostate metabolites using H-1 HR-MAS spectroscopy. Magn Reson Med 55: 1257–1264

    Article  CAS  PubMed  Google Scholar 

  35. Tamayo P, Slonim D, Mesirov J, et al. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96: 2907–2912

    Article  CAS  PubMed  Google Scholar 

  36. Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemometrics 16: 119–128

    Article  CAS  Google Scholar 

  37. Ulrich EL, Akutsu H, Doreleijers JF, et al. (2008) BioMagResBank. Nucleic Acids Res 36: D402–D408

    Article  CAS  PubMed  Google Scholar 

  38. Warburg O (1956) Origin of cancer cells. Science 123: 309–314

    Article  CAS  PubMed  Google Scholar 

  39. Wishart DS (2007) Current progress in computational metabolomics. Brief Bioinform 8: 279–293

    Article  CAS  PubMed  Google Scholar 

  40. Wishart DS, Knox C, Guo AC, et al. (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37: D603–D610

    Article  CAS  PubMed  Google Scholar 

  41. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics Intellig Lab Syst 2: 37–52

    Article  CAS  Google Scholar 

  42. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics Intellig Lab Syst 58: 109–130

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. Trédan.

About this article

Cite this article

Jobard, E., Trédan, O., Elena, B. et al. La métabolomique: un nouvel outil pour la recherche translationnelle en cancérologie. Oncologie 12, 409–415 (2010). https://doi.org/10.1007/s10269-010-1913-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10269-010-1913-8

Keywords

Mots clés

Navigation