Brain Tumor Typing and Therapy Using Combined Ex Vivo Magnetic Resonance Spectroscopy and Molecular Genomics

  • Loukas G. Astrakas
  • A. Aria Tzika
Part of the Tumors of the Central Nervous System book series (TCNS, volume 12)


A novel approach was developed that combines biomarkers detected with magnetic resonance spectroscopy (MRS) and molecular genomics to improve the typing and prognostication of biospecimens in clinical medicine. Metabolite and genome wide profiles from 55 biopsies from subjects with brain tumors were analyzed with a classification algorithm that produces unique tumor fingerprints. We found that the fusion of 15 gene expressions and 15 MRS metabolites were able to distinguish tumor categories and predict survival better than when either dataset was used alone. Our approach improves the typing and understanding of the complexity of human brain tumors, generates testable hypotheses regarding neoplasia and promises to guide human brain tumor therapy. Our results further elucidate the biology of brain malignancy subtypes in brain tumor patients, and increase the overall potential for success of future studies that combine clinical MRI, MRS and MR imaging of gene expression in vivo.


Nuclear Magnetic Resonance Support Vector Machine Magnetic Resonance Spectroscopy Linear Discriminant Analysis Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andronesi OC, Blekas KD, Mintzopoulos D, Astrakas L, Black PM, Tzika AA (2008) Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers. Int J Oncol 33:1017–1025PubMedCentralPubMedGoogle Scholar
  2. Astrakas L, Blekas KD, Constantinou C, Andronesi OC, Mindrinos MN, Likas AC, Rahme LG, Black PM, Marcus KJ, Tzika AA (2011) Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone. Int J Oncol 38:1113–1127PubMedGoogle Scholar
  3. Chang L, McBride D, Miller BL, Cornford M, Booth RA, Buchthal SD, Ernst TM, Jenden D (1995) Localized in vivo 1H magnetic resonance spectroscopy and in vitro analyses of heterogeneous brain tumors. J Neuroimaging 5:157–163PubMedGoogle Scholar
  4. Cheng LL, Anthony DC, Comite AR, Black PM, Tzika AA, Gonzalez RG (2000) Quantification of microheterogeneity in glioblastoma multiforme with ex vivo high-resolution magic-angle spinning (HRMAS) proton magnetic resonance spectroscopy. Neuro Oncol 2:87–95PubMedCentralPubMedGoogle Scholar
  5. Daly PF, Cohen JS (1989) Magnetic resonance spectroscopy of tumors and potential in vivo clinical applications: a review. Cancer Res 49:770–779PubMedGoogle Scholar
  6. DeFeo EM, Cheng LL (2010) Characterizing human cancer metabolomics with ex vivo 1H HRMAS MRS. Technol Cancer Res Treat 9:381–391PubMedCrossRefGoogle Scholar
  7. Duarte IF, Gil AM (2012) Metabolic signatures of cancer unveiled by NMR spectroscopy of human biofluids. Prog Nucl Magn Reson Spectrosc 62:51–74PubMedCrossRefGoogle Scholar
  8. Ermolaeva O, Rastogi M, Pruitt KD, Schuler GD, Bittner ML, Chen Y, Simon R, Meltzer P, Trent JM, Boguski MS (1998) Data management and analysis for gene expression arrays. Nat Genet 20:19–23PubMedCrossRefGoogle Scholar
  9. Garzon B, Emblem KE, Mouridsen K, Nedregaard B, Due-Tonnessen P, Nome T, Hald JK, Bjornerud A, Haberg AK, Kvinnsland Y (2011) Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction. Acta Radiol 52:1052–1060PubMedCrossRefGoogle Scholar
  10. Glunde K, Bhujwalla ZM (2011) Metabolic tumor imaging using magnetic resonance spectroscopy. Semin Oncol 38:26–41PubMedCentralPubMedCrossRefGoogle Scholar
  11. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537PubMedCrossRefGoogle Scholar
  12. Hanchuan P, Fuhui L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238CrossRefGoogle Scholar
  13. Horska A, Barker PB (2010) Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am 20:293–310PubMedCentralPubMedCrossRefGoogle Scholar
  14. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13:415–425PubMedCrossRefGoogle Scholar
  15. Huang Y, Lisboa PJ, El-Deredy W (2003) Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22:147–164PubMedCrossRefGoogle Scholar
  16. Inza I, Larranaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med 31:91–103PubMedCrossRefGoogle Scholar
  17. Macgregor PF, Squire JA (2002) Application of microarrays to the analysis of gene expression in cancer. Clin Chem 48:1170–1177PubMedGoogle Scholar
  18. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2:418–427PubMedCrossRefGoogle Scholar
  19. Reynolds GM, Peet AC, Arvanitis TN (2007) Generating prior probabilities for classifiers of brain tumours using belief networks. BMC Med Inform Decis Mak 7:27PubMedCentralPubMedCrossRefGoogle Scholar
  20. Sakariassen PO, Immervoll H, Chekenya M (2007) Cancer stem cells as mediators of treatment resistance in brain tumors: status and controversies. Neoplasia 9:882–892PubMedCentralPubMedCrossRefGoogle Scholar
  21. Schulze A, Downward J (2001) Navigating gene expression using microarrays–a technology review. Nat Cell Biol 3:E190–E195PubMedCrossRefGoogle Scholar
  22. Tate AR, Majos C, Moreno A, Howe FA, Griffiths JR, Arus C (2003) Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med 49:29–36PubMedCrossRefGoogle Scholar
  23. Tzika AA, Zurakowski D, Poussaint TY, Goumnerova L, Astrakas LG, Barnes PD, Anthony DC, Billett AL, Tarbell NJ, Scott RM et al (2001) Proton magnetic spectroscopic imaging of the child’s brain: the response of tumors to treatment. Neuroradiology 43:169–177PubMedCrossRefGoogle Scholar
  24. Urenjak J, Williams SR, Gadian DG, Noble M (1992) Specific expression of N-acetylaspartate in neurons, oligodendrocyte-type-2 astrocyte progenitors, and immature oligodendrocytes in vitro. J Neurochem 59:55–61PubMedCrossRefGoogle Scholar
  25. Wei SJ, Chao Y, Hung YM, Lin WC, Yang DM, Shih YL, Ch’ang LY, Whang-Peng J, Yang WK (1998) S- and G2-phase cell cycle arrests and apoptosis induced by ganciclovir in murine melanoma cells transduced with herpes simplex virus thymidine kinase. Exp Cell Res 241:66–75PubMedCrossRefGoogle Scholar
  26. Zarbo RJ, Meier FA, Raab SS (2005) Error detection in anatomic pathology. Arch Pathol Lab Med 129:1237–1245PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Medical Physics, Medical SchoolUniversity of IoanninaloanninaGreece
  2. 2.NMR Surgical Laboratory, Department of SurgeryMassachusetts General Hospital and Shriners Burns Institute Harvard Medical SchoolBostonUSA

Personalised recommendations