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Strategy for automated metabolic profiling of glioma subtypes from ex-vivo HRMAS spectra

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Abstract

Introduction

Infiltrating gliomas are primary brain tumors that express significant biological and clinical heterogeneity in adults, which complicates their treatment and prognosis. Characterization of tumor subtypes using spectroscopic analysis may assist in predicting malignant transformation and quantification of response to therapy.

Study objective

To implement an automated algorithm for classification of metabolomic profiles for the classification of glioma pathological grades and the prediction of malignant progression using spectra obtained by high-resolution magic angle spinning (HR-MAS) spectroscopy of patient-derived tissue samples.

Methods

237 image-guided tissue samples were obtained from 152 patients who underwent surgery for newly diagnosed or recurrent glioma and analyzed via HR-MAS spectroscopy. Orthogonal projection to latent structures discriminant analysis was used as a classifier and the variable-influence-on-projection values were evaluated to identify signature spectral regions.

Results

The accuracy of classifiers developed for discriminating glioma subtypes was 68% for newly diagnosed grade II versus III samples; 86 and 92% for new and recurrent grade III versus IV, respectively; 95% for newly diagnosed grade II versus IV; and 88% for recurrent grade II versus IV lesions. Classifiers distinguished between samples from newly diagnosed vs. recurrent lesions with an accuracy of 78% for grade III and 99% for grade IV glioma.

Conclusion

Classifying metabolomic profiles for new and recurrent glioma without prior assumptions regarding spectral components identified candidate in vivo biomarkers for use in assessing changes that are likely to impact treatment decisions.

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References

  • Albers, M. J., Butler, T. N., Rahwa, I., Bao, N., Keshari, K. R., Swanson, M. G., et al. (2009). Evaluation of the ERETIC method as an improved quantitative reference for 1H HR-MAS spectroscopy of prostate tissue. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine, 61(3), 525–532. doi:10.1002/mrm.21808.

    Article  CAS  Google Scholar 

  • Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations Newsletter, 6(1), 20–29. doi:10.1145/1007730.1007735.

    Article  Google Scholar 

  • Blekherman, G., Laubenbacher, R., Cortes, D. F., Mendes, P., Torti, F. M., Akman, S., et al. (2011). Bioinformatics tools for cancer metabolomics. Metabolomics, 7(3), 329–343. doi:10.1007/s11306-010-0270-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Castillo, M., Smith, J. K., & Kwock, L. (2000). Correlation of Myo-inositol Levels and Grading of Cerebral Astrocytomas. American Journal of Neuroradiology, 21(9), 1645–1649.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Central Brain Tumor Registry of the United States. (2015). Accessed October 30, 2015 from http://www.cbtrus.org/factsheet/factsheet.html.

  • Christiansen, P., Toft, P., Larsson, H. B. W., Stubgaard, M., & Henriksen, O. (1993). The concentration of N-acetyl aspartate, creatine + phosphocreatine, and choline in different parts of the brain in adulthood and senium. Magnetic Resonance Imaging, 11(6), 799–806. doi:10.1016/0730-725X(93)90197-L.

    Article  CAS  PubMed  Google Scholar 

  • Elkhaled, A., Jalbert, L. E., Phillips, J. J., Yoshihara, H. A. I., Parvataneni, R., Srinivasan, R., et al. (2012). Magnetic resonance of 2-hydroxyglutarate in IDH1-mutated low-grade gliomas. Science Translational Medicine. doi:10.1126/scitranslmed.3002796.

    Article  PubMed  PubMed Central  Google Scholar 

  • Krex, D., Klink, B., Hartmann, C., von Deimling, A., Pietsch, T., Simon, M., et al. (2007). Long-term survival with glioblastoma multiforme. Brain: A Journal of Neurology, 130(10), 2596–2606. doi:10.1093/brain/awm204.

    Article  Google Scholar 

  • Lai, H. S., Lee, J. C., Lee, P. H., Wang, S. T., & Chen, W. J. (2005). Plasma free amino acid profile in cancer patients. Seminars in Cancer Biology, 15(4), 267–276. doi:10.1016/j.semcancer.2005.04.003.

    Article  CAS  PubMed  Google Scholar 

  • Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., et al. (2016). The 2016 World Health Organization classification of tumors of the central nervous system: A summary. Acta Neuropathologica, 131(6), 803–820. doi:10.1007/s00401-016-1545-1.

    Article  PubMed  Google Scholar 

  • McKnight, T. R., Noworolski, S. M., Vigneron, D. B., & Nelson, S. J. (2001). An automated technique for the quantitative assessment of 3D-MRSI data from patients with glioma. Journal of Magnetic Resonance Imaging, 13(2), 167–177. doi:10.1002/1522-2586(200102)13:2<167::AID-JMRI1026>3.0.CO;2-K.

    Article  CAS  PubMed  Google Scholar 

  • Nelson, S. J. (2004). Magnetic resonance spectroscopic imaging. Engineering in Medicine and Biology Magazine, IEEE, 23(5), 30–39. doi:10.1109/MEMB.2004.1360406.

    Article  Google Scholar 

  • Nelson, S. J. (2011). Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI. NMR in Biomedicine, 24(6), 734–749. doi:10.1002/nbm.1669.

    Article  PubMed  PubMed Central  Google Scholar 

  • Posse, S., Otazo, R., Dager, S. R., & Alger, J. (2013). MR spectroscopic imaging: Principles and recent advances. Journal of Magnetic Resonance Imaging, 37(6), 1301–1325. doi:10.1002/jmri.23945.

    Article  PubMed  Google Scholar 

  • Rees, J., Watt, H., Jäger, H. R., Benton, C., Tozer, D., Tofts, P., et al. (2009). Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation. European Journal of Radiology, 72(1), 54–64. doi:10.1016/j.ejrad.2008.06.013.

    Article  PubMed  Google Scholar 

  • Soher, B. J., van Zijl, P. C. M., Duyn, J. H., & Barker, P. B. (1996). Quantitative proton MR spectroscopic imaging of the human brain. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine, 35(3), 356–363. doi:10.1002/mrm.1910350313.

    Article  CAS  Google Scholar 

  • Tessem, M.-B., Swanson, M. G., Keshari, K. R., Albers, M. J., Joun, D., Tabatabai, Z. L., et al. (2008). Evaluation of lactate and alanine as metabolic biomarkers of prostate cancer using (1)H HR-MAS Spectroscopy of Biopsy Tissues. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine, 60(3), 510–516. doi:10.1002/mrm.21694.

    Article  CAS  Google Scholar 

  • Thevenot, E. A., Roux, A., Xu, Y., Ezan, E., & Junot, C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS Statistical analyses. Journal of Proteome Research, 14(8), 3322–3335. doi:10.1021/acs.jproteome.5b00354.

    Article  CAS  PubMed  Google Scholar 

  • Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119–128. doi:10.1002/cem.695.

    Article  CAS  Google Scholar 

  • Ullrich, R. T., Kracht, L. W., & Jacobs, A. H. (2008). Neuroimaging in Patients with Gliomas. Seminars in Neurology, 28(04), 484–494. doi:10.1055/s-0028-1083696.

    Article  PubMed  Google Scholar 

  • Wiklund, S. (2008). Multivariate data analysis for Omics. Umeå: Umetrics.

    Google Scholar 

  • Worley, B., & Powers, R. (2013). Multivariate analysis in metabolomics. Current Metabolomics, 1(1), 92–107. doi:10.2174/2213235X11301010092.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wright, A. J., Fellows, G. A., Griffiths, J. R., Wilson, M., Bell, B. A., & Howe, F. A. (2010). Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers. Molecular Cancer, 9(1), 66. doi:10.1186/1476-4598-9-66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu, H., & Barker, P. (2011). MR spectroscopy and spectroscopic imaging of the brain. In M. Modo & J. W. M. Bulte (Eds.), Magnetic resonance neuroimaging. Methods in molecular biology (Vol. 711, pp. 203–226) New York: Humana Press.

    Chapter  Google Scholar 

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Funding

This study was funded by a research fellowship provided by the German research foundation (DFG, MA 7292/1-1), NIH Brain Tumor SPORE P50 CA097257, NIH PO1 CA118816, and NIH RO1 CA127612.

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Authors and Affiliations

  1. Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA

    Stojan Maleschlijski, Adam Autry, Llewellyn Jalbert, Marram P. Olson, Tracy McKnight, Tracy Luks & Sarah Nelson

  2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    Sarah Nelson

  3. UCSF - Mission Bay, University of California San Francisco, Byers Hall, Room BH-303, 1700 4th Street, MC 2532, San Francisco, CA, 94158-2330, USA

    Sarah Nelson

Authors
  1. Stojan Maleschlijski
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  2. Adam Autry
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  3. Llewellyn Jalbert
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  4. Marram P. Olson
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  5. Tracy McKnight
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  6. Tracy Luks
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  7. Sarah Nelson
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Corresponding author

Correspondence to Sarah Nelson.

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Conflict of interest

The authors declare no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This study was approved by the Institutional Review Board (IRB) at UCSF and informed consent was obtained from all individual participants included in the study.

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Maleschlijski, S., Autry, A., Jalbert, L. et al. Strategy for automated metabolic profiling of glioma subtypes from ex-vivo HRMAS spectra. Metabolomics 13, 149 (2017). https://doi.org/10.1007/s11306-017-1285-9

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