"Aerobic glycolytic imaging" of human gliomas using combined pH-, oxygen-, and perfusion-weighted magnetic resonance imaging

Akifumi Hagiwara, Jingwen Yao, Catalina Raymond, Nicholas S Cho, Richard Everson, Kunal Patel, Danielle H Morrow, Brandon R Desousa, Sergey Mareninov, Saewon Chun, David A Nathanson, William H Yong, Gafita Andrei, Ajit S Divakaruni, Noriko Salamon, Whitney B Pope, Phioanh L Nghiemphu, Linda M Liau, Timothy F Cloughesy, Benjamin M Ellingson, Akifumi Hagiwara, Jingwen Yao, Catalina Raymond, Nicholas S Cho, Richard Everson, Kunal Patel, Danielle H Morrow, Brandon R Desousa, Sergey Mareninov, Saewon Chun, David A Nathanson, William H Yong, Gafita Andrei, Ajit S Divakaruni, Noriko Salamon, Whitney B Pope, Phioanh L Nghiemphu, Linda M Liau, Timothy F Cloughesy, Benjamin M Ellingson

Abstract

Purpose: To quantify abnormal metabolism of diffuse gliomas using "aerobic glycolytic imaging" and investigate its biological correlation.

Methods: All subjects underwent a pH-weighted amine chemical exchange saturation transfer spin-and-gradient-echo echoplanar imaging (CEST-SAGE-EPI) and dynamic susceptibility contrast perfusion MRI. Relative oxygen extraction fraction (rOEF) was estimated as the ratio of reversible transverse relaxation rate R2' to normalized relative cerebral blood volume. An aerobic glycolytic index (AGI) was derived by the ratio of pH-weighted image contrast (MTRasym at 3.0 ppm) to rOEF. AGI was compared between different tumor types (N = 51, 30 IDH mutant and 21 IDH wild type). Metabolic MR parameters were correlated with 18F-FDG uptake (N = 8, IDH wild-type glioblastoma), expression of key glycolytic proteins using immunohistochemistry (N = 38 samples, 21 from IDH mutant and 17 from IDH wild type), and bioenergetics analysis on purified tumor cells (N = 7, IDH wild-type high grade).

Results: AGI was significantly lower in IDH mutant than wild-type gliomas (0.48 ± 0.48 vs. 0.70 ± 0.48; P = 0.03). AGI was strongly correlated with 18F-FDG uptake both in non-enhancing tumor (Spearman, ρ = 0.81; P = 0.01) and enhancing tumor (ρ = 0.81; P = 0.01). AGI was significantly correlated with glucose transporter 3 (ρ = 0.71; P = 0.004) and hexokinase 2 (ρ = 0.73; P = 0.003) in IDH wild-type glioma, and monocarboxylate transporter 1 (ρ = 0.59; P = 0.009) in IDH mutant glioma. Additionally, a significant correlation was found between AGI derived from bioenergetics analysis and that estimated from MRI (ρ = 0.79; P = 0.04).

Conclusion: AGI derived from molecular MRI was correlated with glucose uptake (18F-FDG and glucose transporter 3/hexokinase 2) and cellular AGI in IDH wild-type gliomas, whereas AGI in IDH mutant gliomas appeared associated with monocarboxylate transporter density.

Keywords: (18)FDG-PET; Aerobic glycolysis; Glioblasoma; Glioma; IDH; amine CEST.

Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

ASD has previously served as a paid consultant to Agilent Technologies. BME is on advisory board of Hoffman La-Roche, Siemens, Nativis, Medicenna, MedQIA, Bristol Meyers Squibb, Imaging Endpoints, and Agios. BME is a paid consultant of Nativis, MedQIA, Siemens, Hoffman La-Roche, Imaging Endpoints, Medicenna, and Agios. BME has grant funding by Hoffman La-Roche, Siemens, Agios, and Janssen. BME holds a patent on this technology (US Patent #15/577664; International PCT/US2016/034886).

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Fig. 1
Fig. 1
Schematic overview of metabolic pathway in IDH wild-type and mutant gliomas. Similar to other malignant tumors, IDH wild-type glioma is characterized by a high level of aerobic glycolysis (Warburg effect). Hypoxia induces upregulation of HIF1α, leading to overexpression of glycolysis-related proteins/enzymes, namely, GLUT, HK2, MCT, and LDHA, thereby accelerating glycolysis. On the other hand, HIF1α response to hypoxia is blunted in IDH mutant glioma, shifting the metabolism to oxidative phosphorylation. In this study, the extent of aerobic glycolysis was estimated by pH- and oxygen-sensitive imaging combined with perfusion imaging. The proteins/enzymes investigated in our study are labeled in black. Notably, MTRasym at 3.0 ppm increases not only in accordance with decrease in pH but also with increase in glutamine related to tumor activity. 2HG = 2-hydroxyglutarate, aKG = alpha-ketoglutarate, acetyl-CoA = acetyl coenzyme A, G6P = glucose-6-phosphate, GDH = glutamate dehydrogenase, GLS = glutaminase, GLUT = glucose transporter, HK2 = hexokinase 2, LAT = L-amino acid transporter, LDHA = lactic dehydrogenase A, MCT = monocarboxylate transporter, mTOR = mammalian target of rapamycin, PDH = pyruvate dehydrogenase, PDK1 = pyruvate dehydrogenase kinase 1, ROS = reactive oxygen species, TA = transaminase, TCA cycle = tricarboxylic acid cycle.
Fig. 2
Fig. 2
Patients with diffuse gliomas with high (A, from Study II), medium (B, from Study II), and low AGI (C, from Study I), and high (A) and low (B) 18F-FDG uptake. IDH wild-type glioblastomas (A and B) show higher AGI than IDH mutant grade II glioma (C). Contrast-enhancing region of the tumor is showing higher AGI than non-contrast-enhancing region of the tumor (A and B). MTRasym at 3.0 ppm is grossly high in the entire tumors for both glioblastomas, while AGI seems to be correlated more with 18F-FDG PET than MTRasym at 3.0 ppm. While 18F-FDG PET shows high physiological uptake in the cortex, quantitative maps, including AGI, derived from MRI do not show high values in the cortex. NAWM = normal-appearing white matter.
Fig. 3
Fig. 3
Comparison of AGI between different tumor and tissue types. Box plots are overlaid on violin plots. (A) Comparison between IDH mutant and wild type. Among IDH mutant gliomas, those with AGI greater than 1 are mostly high grade (4 out of 5). (B) Comparison between low-grade and high-grade gliomas. (C) Comparison across contrast-enhancing tumor, non-enhancing tumor, and normal-appearing white matter. * P < 0.05, ** P < 0.01 *** P < 0.001.
Fig. 4
Fig. 4
Scatterplots of AGI compared with normalized 18F-FDG in the contrast-enhancing plus non-enhancing (A), contrast-enhancing (B), and non-enhancing (C) portions of IDH wild-type glioblastoma and (D) the whole brain. Lines show linear regression fit to the data. Strong correlations were found in all tumor areas, while no significant correlation was found in the whole brain.
Fig. 5
Fig. 5
MR image and AGI map and corresponding H&E and immunohistochemistry staining for MRI-guided biopsy targets (circles). (A) Treatment-naïve IDH wild-type glioblastoma for which an area with low AGI was biopsied. Expressions of GLUT3 and HK2 are low in the slides from a 5-mm radius sample taken from the MRI-guided biopsy target. (B) Treatment naïve IDH wild-type glioblastoma for which an area with high AGI was biopsied. Expressions of GLUT3 and HK2 are high. (C) Post-surgical, chemoradiotherapy-naïve IDH mutant WHO grade III glioma for which an area with low AGI was biopsied. Expression of MCT1 is low. (D) Treatment-naïve IDH mutant WHO grade III glioma for which an area with high AGI was biopsied. Expression of MCT1 is high. For GLUT3, HK2, and MCT1 stains, cells positive for expression are brown. GLUT3 = glucose transporter 3, HK2 = hexokinase 2, MCT1 = monocarboxylate transporter 1.
Fig. 6
Fig. 6
(A) Correlation matrix of AGI compared with immunohistochemistry measurements shown with Spearman’s correlation coefficients. * P < 0.05, ** P < 0.01. Scatterplots of AGI compared with immunohistochemistry measurement of GLUT3 (B), HK2 (C), and MCT1 (D). Lines show linear regression fit to the data. GLUT3 = glucose transporter 3; HK2 = hexokinase 2, LDHA = lactate dehydrogenase A, MCT1 = monocarboxylate transporter 1.
Fig. 7
Fig. 7
MR images/maps of two patients with IDH wild-type glioblastoma that showed higher (A) and lower (B) AGI derived from MRI and extracellular flux bioenergetic analysis. An ROI of contrast-enhancing portion is overlaid on each image/map. (C), (D) Bioenergetic analysis shows higher ECAR and lower basal OCR in (A), leading to higher cellular AGI that explains higher AGI derived from MRI, whereas ECAR and OCR are lower and higher, respectively, for (B), leading to lower cellular AGI that explains lower AGI derived from MRI.
Fig. 8
Fig. 8
Scatterplot of AGI compared with cellular AGI derived from bioenergetic extracellular flux analysis. Lines show linear regression fit to the data. A strong correlation was found between AGI and cellular AGI.

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