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. 2013 Jan 22;14(1):2104-18.
doi: 10.3390/ijms14012104.

Metabolic profiles of brain metastases

Torill E Sjøbakk  1 , Riyas Vettukattil, Michel Gulati, Sasha Gulati, Steinar Lundgren, Ingrid S Gribbestad, Sverre H Torp, Tone F Bathen
Affiliations

Metabolic profiles of brain metastases

Torill E Sjøbakk et al. Int J Mol Sci. .

Abstract

Metastasis to the brain is a feared complication of systemic cancer, associated with significant morbidity and poor prognosis. A better understanding of the tumor metabolism might help us meet the challenges in controlling brain metastases. The study aims to characterize the metabolic profile of brain metastases of different origin using high resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) to correlate the metabolic profiles to clinical and pathological information. Biopsy samples of human brain metastases (n = 49) were investigated. A significant correlation between lipid signals and necrosis in brain metastases was observed (p < 0.01), irrespective of their primary origin. The principal component analysis (PCA) showed that brain metastases from malignant melanomas cluster together, while lung carcinomas were metabolically heterogeneous and overlap with other subtypes. Metastatic melanomas have higher amounts of glycerophosphocholine than other brain metastases. A significant correlation between microscopically visible lipid droplets estimated by Nile Red staining and MR visible lipid signals was observed in metastatic lung carcinomas (p = 0.01), indicating that the proton MR visible lipid signals arise from cytoplasmic lipid droplets. MRS-based metabolomic profiling is a useful tool for exploring the metabolic profiles of metastatic brain tumors.

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Figures

Figure 1
Figure 1
Representative proton HR-MAS spectra from a patient with brain metastasis from lung carcinoma obtained as (a) pre-saturated, single-pulse spectrum and (b) spin-echo spectrum, showing assignment of various metabolites. The enlarged ppm region 3.6–3.0 ppm shows more details of glycine (Gly), myo-inositol (myo-In), taurine (Tau), scyllo-inositol (scy-In), glycerophosphocholine (GPC), phosphocholine (PCho), choline (Cho) and creatine (Cr).
Figure 2
Figure 2
(a) Principal component analysis (PCA) score plot of PC-1 (explained variance 71%) and PC-3 (explained variance 4%) based on single-pulse spectra (n = 39) showing the distribution of the metastases from four different primary cancer diagnoses; lung (blue), breast (red), colorectal (green) and melanoma carcinomas (yellow). The light blue colored part of the circles reflects the content of necrotic tissue determined by HES staining post HR-MAS; (b) Loading plots for PC-1 and PC-3; Samples with high score values for PC-1 have higher lipid levels. High score values for PC-3 are mainly due to high levels of phosphocholine (PCho).
Figure 3
Figure 3
Nile Red fluorescent-stained images showing lipid droplets in three brain metastases originating from lung carcinoma (A, B, C) and their corresponding mean normalized single-pulse spectra (1.6–0.8 ppm), which demonstrate signal differences due to methylene (1.3 ppm) and methyl groups (0.9 ppm) in the mobile fatty acyl chains.
Figure 4
Figure 4
Distribution of lipid droplets for droplet area between 0 and 1.005 μm2 in bins of 0.015 μm2. The x-axis represents droplet area distribution and the y-axis—droplet density (number LD/mm2). The red, purple and blue bars represent the mean number of LD in high (>70%) medium (40%–70%) and low (<40%) necrosis, respectively.
Figure 5
Figure 5
(a) PCA score plot showing PC-1 (42%) and PC-2 (15%) of cpmgpr spectra (ppm region 4.68–2.98) from brain metastases with less than 50% necrotic tissue (n = 26); (b) The loading plot of PC-2 indicates the metabolites GPC, Cr, Tau, Gly, Cho and Lac as important signals that divide the subtypes. The positive dominating signal in the loading plot for PC-1 (not shown) was PCho, explaining the dispersion in PC-1 direction in the score plot.
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