Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI
Yue Zhang, Durga Udayakumar, Ling Cai, Zeping Hu, Payal Kapur, Eun-Young Kho, Andrea Pavía-Jiménez, Michael Fulkerson, Alberto Diaz de Leon, Qing Yuan, Ivan E. Dimitrov, Takeshi Yokoo, Jin Ye, Matthew A. Mitsche, Hyeonwoo Kim, Jeffrey G. McDonald, Yin Xi, Ananth J. Madhuranthakam, Durgesh K. Dwivedi, Robert E. Lenkinski, Jeffrey A. Cadeddu, Vitaly Margulis, James Brugarolas, Ralph J. DeBerardinis, Ivan Pedrosa
Yue Zhang, Durga Udayakumar, Ling Cai, Zeping Hu, Payal Kapur, Eun-Young Kho, Andrea Pavía-Jiménez, Michael Fulkerson, Alberto Diaz de Leon, Qing Yuan, Ivan E. Dimitrov, Takeshi Yokoo, Jin Ye, Matthew A. Mitsche, Hyeonwoo Kim, Jeffrey G. McDonald, Yin Xi, Ananth J. Madhuranthakam, Durgesh K. Dwivedi, Robert E. Lenkinski, Jeffrey A. Cadeddu, Vitaly Margulis, James Brugarolas, Ralph J. DeBerardinis, Ivan Pedrosa
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Clinical Research and Public Health Metabolism Oncology

Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI

Abstract

BACKGROUND. Dysregulated lipid and glucose metabolism in clear cell renal cell carcinoma (ccRCC) has been implicated in disease progression, and whole tumor tissue–based assessment of these changes is challenged by the tumor heterogeneity. We studied a noninvasive quantitative MRI method that predicts metabolic alterations in the whole tumor. METHODS. We applied Dixon-based MRI for in vivo quantification of lipid accumulation (fat fraction [FF]) in targeted regions of interest of 45 primary ccRCCs and correlated these MRI measures to mass spectrometry–based lipidomics and metabolomics of anatomically colocalized tissue samples isolated from the same tumor after surgery. RESULTS. In vivo tumor FF showed statistically significant (P < 0.0001) positive correlation with histologic fat content (Spearman correlation coefficient, ρ = 0.79), spectrometric triglycerides (ρ = 0.56) and cholesterol (ρ = 0.47); it showed negative correlation with free fatty acids (ρ = –0.44) and phospholipids (ρ = –0.65). We observed both inter- and intratumoral heterogeneity in lipid accumulation within the same tumor grade, whereas most aggressive tumors (International Society of Urological Pathology [ISUP] grade 4) exhibited reduced lipid accumulation. Cellular metabolites in tumors were altered compared with adjacent renal parenchyma. CONCLUSION. Our results support the use of noninvasive quantitative Dixon-based MRI as a biomarker of reprogrammed lipid metabolism in ccRCC, which may serve as a predictor of tumor aggressiveness before surgical intervention. FUNDING. NIH R01CA154475 (YZ, MF, PK, IP), NIH P50CA196516 (IP, JB, RJD, JAC, PK), Welch Foundation I-1832 (JY), and NIH P01HL020948 (JGM).

Authors

Yue Zhang, Durga Udayakumar, Ling Cai, Zeping Hu, Payal Kapur, Eun-Young Kho, Andrea Pavía-Jiménez, Michael Fulkerson, Alberto Diaz de Leon, Qing Yuan, Ivan E. Dimitrov, Takeshi Yokoo, Jin Ye, Matthew A. Mitsche, Hyeonwoo Kim, Jeffrey G. McDonald, Yin Xi, Ananth J. Madhuranthakam, Durgesh K. Dwivedi, Robert E. Lenkinski, Jeffrey A. Cadeddu, Vitaly Margulis, James Brugarolas, Ralph J. DeBerardinis, Ivan Pedrosa

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Figure 4

Clear cell renal cell carcinoma (ccRCC) exhibits widespread metabolic alterations compared with uninvolved renal parenchyma (URP).

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Clear cell renal cell carcinoma (ccRCC) exhibits widespread metabolic al...
(A) Heatmap of unsupervised hierarchical clustering of 80 human samples in 23 patients with ccRCC (57 tumors and 23 URP) analyzed for the abundance of 108 metabolites. Each column and row corresponds to 1 tissue sample and a single metabolite, respectively. Yellow cluster represents tumor samples, and the green cluster represents normal samples. (B) Volcano plot analysis of metabolites significantly altered in tumor versus URP. The fold change of metabolite levels is plotted on the x-axis. The FDR-adjusted significance is plotted on the y-axis. Horizontal dash line represent q value cutoff of –log10 (0.05); vertical dash lines represent log2-fold change (tumor/URP) of –1 and +1.71. Metabolites with significantly different levels in tumor vs. URP samples are indicated as blue, light blue, pink, and red dots. With fold-change cutoff of 2, 19 metabolites were decreased (blue) and 14 metabolites were increased (red) in tumor vs. URP samples. Black dots represent the metabolites that did not pass the q value cut-off. A two-sided t test was performed, and the hits with P < 0.05 adjusted by Benjamini-Hochberg procedures were considered significant.