An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Abstract

Dysregulated metabolism is a hallmark of cancer, manifested through alterations in metabolites. We performed metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs and found that ccRCC is characterized by broad shifts in central carbon metabolism, one-carbon metabolism, and antioxidant response. Tumor progression and metastasis were associated with metabolite increases in glutathione and cysteine/methionine metabolism pathways. We develop an analytic pipeline and visualization tool (metabolograms) to bridge the gap between TCGA transcriptomic profiling and our metabolomic data, which enables us to assemble an integrated pathway-level metabolic atlas and to demonstrate discordance between transcriptome and metabolome. Lastly, expression profiling was performed on a high-glutathione cluster, which corresponds to a poor-survival subgroup in the ccRCC TCGA cohort.

Figure 1. Clinical and metabolic features of the MSK ccRCC Metabolomics Cohort

(A) Clinical characteristics of the patient cohort at presentation. Among the 118 patients who presented with Stage I-III diseases, 19 (16.1%) developed a new recurrence by the end of 2014. (B) A volcano plot of the 577 named metabolites profiled. 319 exhibited significant differential abundance (p value <0.001, absolute fold change > 2) when comparing ccRCC tumors to adjacent normal kidney tissues. Mann-Whitney U tests were used to calculate statistical significance, and p values were corrected using the Benjamini-Hochberg procedure. Differentially abundant metabolites of different categories were individually color-coded. See also Table S1 and S2, Figure S1.

Figure 2. Pathway-based analysis of ccRCC metabolomics

(A) A pathway-based analysis of metabolic changes upon comparing primary ccRCC to adjacent normal kidney tissues. The differential abundance score captures the average, gross changes for all metabolites in a pathway. A score of 1 indicates all measured metabolites in the pathway increase, and −1 indicates all measured metabolites in a pathway decrease. # the amino acid pathways. * glucose metabolism. (B) Metabolic changes of central carbon metabolism in ccRCC. Metabolites are labeled as color-coded ovals. Color corresponds to the log₂ fold changes between tumor and normal tissues. Red, increase; Blue, decrease; Green, isomers; Gray, not measured. Enzymes for individual chemical reactions were denoted next to arrows connecting two metabolites. See also Table S3, and Figure S2.

Figure 3. Metabolites associated with ccRCC progression

(A) Mann-Whitney U tests were used to identify metabolites significantly higher or lower in Stage III/IV tumors, compared to Stage I/II tumors (Benjamini-Hochberg corrected p value < 0.05, absolute log₂ fold change > 2). Metabolites were grouped, labeled on the left and detailed on the right. Clinical stages at presentation were color-labeled. Recurrences (n = 19) in the original Stage I to III patients (n = 118) were marked as darker gray bars. (B) Metabolic shifts in the TCA cycle and fatty acids during the progression. (C) Depicted are metabolic shifts of several interconnected metabolic programs upon ccRCC progression, including folate/methionine cycle, glutathione metabolism, and polyamine/urea metabolism. (B, C) Color corresponds to the log₂ fold change between high stage (III/IV) and low stage (I/II) disease. Red, increase; Blue, decrease; Gray, not measured. Metabolites are labeled as color-coded ovals. Enzymes for individual chemical reactions were denoted next to arrows connecting two metabolites. See also Table S4.

Figure 4. Unsupervised clustering of ccRCC based on metabolite signatures

(A) Nonnegative matrix factorization (NMF) clustering of metabolomics data. Note that consensus results show consistency for k=4. (B) Mann-Whitney U-tests were used to calculate which metabolites were significantly increased or decreased in each cluster, relative to all other tumors (Benjamini-Hochberg corrected p value < 0.05). X-axis indicates, for a given cluster of patient samples, the proportion of metabolites in a pathway that are significantly changed (both increased and decreased) in a cluster. Y-axis plots the average log₂ fold change of these metabolites. mCluser 2 & mCluser 4 are enriched in either increase or decrease of metabolites concerning glutathione metabolism, respectively. mCluster 1 & 3 show large differences in dipeptide levels, relative to other tumor samples. (C) The clinical stages at sample collection and the eventual metastasis of each individual metabolic cluster are presented. mCluster 1 is particularly enriched with Stage 1 tumors (p<0.0001 Chi-Square). (D) Comparison of metabolite abundances in tumors developing metastases versus those not developing metastases at preparation of this report. Red, the metabolites in the glutathione biosynthetic pathway that are increased in tumors that developed metastases. Blue dots correspond to dipeptides. See also Table S5 and S6 and Figure S3.

Figure 5. Correlation between the KIRC TCGA transcriptomics and the MSK ccRCC global metabolomics

(A) For each KEGG pathway, the average fold changes of all genes were calculated. A differential abundance score was subsequently calculated for each pathway, which equals to the proportion of genes significantly increased in abundance in the pathway (FDR p value < 0.05) minus the proportion of genes significantly decreased in the pathway. The process was repeated for all metabolites in each pathway, and the two scores were plotted against each other. Size of dots indicates number of quantified metabolites in the pathway. (B) Detailed network map of central carbon highlights discordant behavior of metabolite and gene expression levels in ccRCC tumors. Coloring corresponds to the log₂ fold change between tumor and normal tissue. Ovals represent metabolites (TKCRP ccRCC Metabolomics Cohort) and rectangles represent mRNA levels (KIRC TCGA RNA-Seq). Red, increase; Blue, decrease; Green, isomers; Gray, not measured. See also Figure S4.

Figure 6. Metabolic pathway-based integration of transcriptomics and metabolomics with a novel web-based analytic tool, “Metabologram”

(A, B) Each circular metabologram corresponds to a metabolic pathway. The left half circle corresponds to transcriptomics and the right half circle corresponds to metabolomics. The inner round center corresponds to the average fold change among all constituents of the pathway. The outer circle displays the fold change for each individual gene (left) and metabolite (right). 66 metabolograms from KEGG metabolic pathways are accessible through the web data portal, where interactive features enable detailed exploration by users. Metabolograms illustrate the metabolic differences between kidney tumors and adjacent normal tissue (A) and between late- and early-stage tumors (B). (C) Metabolites indicated by asterisk in (A, B) are displayed in violin plots as a function of normal kidney tissues and tumors at different stages. See also Figure S5.

Figure 7. Mapping the MSK high glutathione ccRCC cluster with the KIRC-TCGA mutli-platform omics dataset

(A) Consensus clustering was performed on 1,506 metabolic genes from the Recon2 human metabolic network reconstruction using RNA-Seq data from the KIRC TCGA (n = 398, gray bars), and the MSK TKCRP ccRCC Metabolomics high-glutathione tumors (n = 10, black bars). Mutations of indicated genes were marked by color bars. Depicted are the top 1000 most variable metabolic genes across the cohort, using log-normalized counts from limma voom. (B) Volcano plots of differentially expressed metabolic genes among four rClusters. HMGCS2, GLYAT, GATM, and ACAT1 are nuclear DNA-encoded mitochondrial genes. (C) Kaplan-Meier curves of cancer specific survival of individual rClusters (p value <0.0001, log-rank test). See also Table S7 and S8 and Figure S6.