Assessing the value of ex vivo high-resolution magic angle spinning 1H magnetic resonance spectroscopy (HRMAS 1H-MRS) in pathological grading of gliomas

2013 ◽  
pp. 129-133
Author(s):  
H Zhang
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Magnetic Resonance Spectroscopy ◽  
Ex Vivo ◽  
Magic Angle Spinning ◽  
Resonance Spectroscopy ◽  
Magic Angle ◽  
1H Mrs ◽  
1H Magnetic Resonance Spectroscopy ◽  
Angle Spinning

Using high-resolution magic angle spinning magnetic resonance spectroscopy to characterize the metabolomic profile of renal cell carcinoma.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 710-710
Author(s):  
Melissa Jessica Huynh ◽  
Andrew Gusev ◽  
Francesco Palmas ◽  
Lindsey Vandergrift ◽  
Chin-Lee Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Magnetic Resonance Spectroscopy ◽  
Renal Cell ◽  
Magic Angle Spinning ◽  
Resonance Spectroscopy ◽  
Magic Angle ◽  
Metabolomic Profile ◽  
Angle Spinning

710 Background: Renal cell carcinoma (RCC) is a metabolic disease, with the various subtypes exhibiting aberrations in different metabolic pathways. Metabolomics may offer greater sensitivity for revealing disease biology than evaluation of tissue morphology. In this study, we investigate the metabolomic profile of RCC using high resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Methods: Tissue samples were obtained from radical or partial nephrectomy specimens that were fresh frozen & stored at -80ºC. Tissue HRMAS MRS was performed by a Bruker AVANCE spectrometer. Metabolomic profiles of RCC & adjacent benign renal tissue were compared, and false discovery rates (FDR) were used to account for multiple testing. Regions of interest (ROI) with FDR < 0.05 were selected as potential predictors of malignancy. The Wilcoxon rank sum test was used to compare median MRS relative intensities for the candidate predictors. Logistic regression was used to determine odds ratios for risk of malignancy based on abundance of each metabolite. Results: There were 38 RCC (16 clear cell, 11 papillary, 11 chromophobe) & 13 adjacent normal tissue specimens (matched pairs). Metabolic predictors of malignancy based on FDR include histidine, phenylalanine, phosphocholine, serine, phosphocreatine, creatine, glycerophosphocholine, valine, glycine, myo-inositol, scylla-inositol, taurine, glutamine, spermine, acetoacetate & lactate. Higher levels of spermine, histidine & phenylalanine at 3.15-3.13 ppm were associated with a decreased risk of RCC (OR 4x10−5, 95% CI 7.42x10−8, 0.02), while 2.84-2.82 ppm increased the risk of malignant pathology (OR 7158.67, 95% CI 6.3, 8.3x106), and the specific metabolites characterizing this region remain to be identified. Tumor stage did not appear to affect the metabolomics of malignant tumors, suggesting that metabolites are more dependent on histologic subtype. Conclusions: HRMAS-MRS identified many metabolites that may predict RCC. We demonstrated that those in the 3.14-3.13 ppm ROI were present in lower levels in RCC, while higher levels of metabolites in the 2.84-2.82 ppm ROI substantially increased the risk of RCC.

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