Research ArticleBrain Imaging

Detection of 2-Hydroxyglutarate in IDH-Mutated Glioma Patients by In Vivo Spectral-Editing and 2D Correlation Magnetic Resonance Spectroscopy

Science Translational Medicine  11 Jan 2012:
Vol. 4, Issue 116, pp. 116ra4
DOI: 10.1126/scitranslmed.3002693

You are currently viewing the editor's summary.

View Full Text

Log in

Spectroscopy Gets Inside Your Head

Gliomas are diffuse brain tumors that are difficult to diagnose, with outcomes that are nearly impossible to predict—unless you can sample the diseased tissue itself via biopsy. This invasive procedure is typically performed at the time of surgery, with results available only after several weeks. Normally, it is a good thing that people can’t “see” inside your head; but, for gliomas, Andronesi and coauthors have found it to be beneficial by noninvasively imaging the brain to identify a glioma gene mutation that is correlated with patient survival.

Mutations in the enzyme isocitrate dehydrogenase (IDH) lead to the accumulation of the metabolite 2-hydroxyglutarate (2HG). This mutation has been found in up to 86% of grade II to IV gliomas. Patients with IDH1 gene mutations have a greater 5-year survival rate than do patients with wild-type IDH1 gliomas, suggesting that such mutations could be used for prognosis. Andronesi et al. developed a strategy to detect IDH1 mutations in patients with glioma using magnetic resonance spectroscopy (MRS) imaging of 2HG. The similarity of 2HG to other metabolites, such as glutamate and glutamine, precludes detection with traditional one-dimensional spectroscopy; however, two-dimensional MRS allowed the authors to see the presence of 2HG in the brains of two glioma patients with IDH1 mutations, but not in healthy volunteers with wild-type IDH. The method was further validated ex vivo in tissue biopsies.

With these results and those in the companion study by Elkhaled et al. (also in this issue), the authors show that in vivo brain imaging for genotyping cancer patients is a possibility—one that would avoid invasive clinical procedures and help doctors not only predict cancer outcomes but also effectively treat tumors on the basis of grade and genetic makeup.