1. Cancer Biology
  2. Genetics and Genomics
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Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma

  1. Jianing Xu
  2. Ed Reznik  Is a corresponding author
  3. Ho-Joon Lee
  4. Gunes Gundem
  5. Philip Jonsson
  6. Judy Sarungbam
  7. Anna Bialik
  8. Francisco Sanchez-Vega
  9. Chad J Creighton
  10. Jake Hoekstra
  11. Li Zhang
  12. Peter Sajjakulnukit
  13. Daniel Kremer
  14. Zachary Tolstyka
  15. Jozefina Casuscelli
  16. Steve Stirdivant
  17. Jie Tang
  18. Nikolaus Schultz
  19. Paul Jeng
  20. Yiyu Dong
  21. Wenjing Su
  22. Emily H Cheng
  23. Paul Russo
  24. Jonathan A Coleman
  25. Elli Papaemmanuil
  26. Ying-Bei Chen
  27. Victor E Reuter
  28. Chris Sander
  29. Scott R Kennedy
  30. James J Hsieh
  31. Costas A Lyssiotis  Is a corresponding author
  32. Satish K Tickoo  Is a corresponding author
  33. A Ari Hakimi  Is a corresponding author
  1. Memorial Sloan Kettering Cancer Center, United States
  2. University of Michigan, United States
  3. Human Genome Sequencing Center, Baylor College of Medicine, United States
  4. Baylor College of Medicine, United States
  5. University of Washington, United States
  6. Ludwig-Maximilians University, Germany
  7. Metabolon Inc, United States
  8. Cedars-Sinai Medical Center, United States
  9. cBio Center, Dana-Farber Cancer Institute, United States
  10. Harvard Medical School, United States
  11. Siteman Cancer Center, Washington University, United States
  12. Rogel Cancer Center, University of Michigan, United States
Research Article
Cite this article as: eLife 2019;8:e38986 doi: 10.7554/eLife.38986
6 figures, 1 table and 1 additional file


Figure 1 with 1 supplement
CCPAP is a metabolic outlier compared to conventional ccRCC.

(A) t-distributed stochastic neighbor embedding (t-SNE) metabolomic data from 140 nominal ccRCC identified two outlier tumors (dataset RC12). Pathological review confirmed that these tumors were clear cell papillary renal cell carcinoma (CCPAP). (B) t-SNE of metabolomic data of a validation cohort (RC13) of 9 CCPAP and 10 ccRCC tumors confirmed that CCPAP tumors cluster distinctly from ccRCC tumors.

Figure 1—figure supplement 1
Principal components analysis of RC13 data.
Figure 2 with 2 supplements
Metabolic landscape of CCPAP.

(A) Heatmap of differentially abundant metabolites among CCPAP (n = 9), ccRCC (n = 10) and matching normal (n = 10). For visual clarity, only metabolites with Mann-Whitney q < 0.1 and absolute log2 fold-change >1 are displayed; a full table of results is available in Figure 2—source data 1. Yellow arrows indicate high-sorbitol ccRCC tumors. (B) Pathway map depicting metabolite changes (tumors relative to normal kidney tissue) in central carbon metabolism. Metabolites in ccRCC (left half of each box) significantly different from CCPAP (right half of each box) are indicated. (C) Relative abundances of key metabolites in CCPAP, ccRCC or normal tissue in polyol pathway. Yellow dots in ccRCC (Log Sorbitol plot) indicate high-sorbitol ccRCC tumors. (D) Relative abundances of reduced and oxidized glutathione (GSH and GSSG) and NADH/NAD ratio in CCPAP, ccRCC or normal tissue. NS, q > 0.1;*,q < 0.1; **, q < 0.01, Mann-Whitney U test., BH-corrected for multiple hypothesis testing.

Figure 2—source data 1

Differential abundance of metabolites.

Figure 2—source data 2

Metabolite information related to metabolomics data in RC13.

Figure 2—figure supplement 1
Additional metabolomic analysis of CCPAP tumors.

(A) Correlation between differential abundance calculations when using either low stage ccRCC (horizontal axis) or all ccRCC (vertical axis) as the reference set. (B) Quantification of sorbitol in 6 CCPAP tumors by two independent measurements from Metabolon (RC13, horizontal axis) and University of Michigan (RC15, vertical axis). In RC15 dataset, four technical replicates were run for each sample, with a line fitted through the mean of the four technical replicates. (C) Correlation of all metabolites measured in both Michigan and RC13 datasets.

Figure 2—figure supplement 2
Comparison of metabolite ratios across different tissue types for metabolites involved in NAD reduction reactions.
Figure 3 with 1 supplement
CCPAP tumors exhibit suppressed mitochondrial respiration.

(A) Plots of log transformed mtDNA copy number of 322 TCGA nominal ccRCC samples. Two misclassified samples (BP-4760 and BP-4795) were pathologically re-confirmed as CCPAP. (B) Quantification of transcription from the mitochondrial genomes of tumor or normal samples from different subtypes of kidney cancers. ***, p<0.001, t-test. (C) Gene set enrichment analysis of transcriptome of CCPAP compared with adjacent normal tissue showing that CCPAP tumors exhibit a downregulation of genes related to mitochondria, and oxidative phosphorylation. (D) The relative abundance of aspartate and fumarate in CCPAP, ccRCC or normal tissue. NS, q > 0.1; **, q < 0.01, Mann-Whitney U test following multiple hypothesis correction.

Figure 3—source data 1

RNA (including miRNA) Sequencing Results.

Figure 3—source data 2

GSEA results.

Figure 3—figure supplement 1
H and E image of 5 misclassified TCGA-KIRC samples, which has been re-evaluated to be CCPAP.
Figure 4 with 1 supplement
CCPAP tumors show few somatic alterations to the nuclear genome, but recurrent depletion of the mitochondrial genome.

(A) Mutational burden of CCPAP tumors from both MSKCC and TCGA as well as ccRCC from TCGA. Highlighted in the boxes are COSMIC cancer gene census genes which are mutated in either MSKCC (red) or TCGA (blue) CCPAP tumors. Notably, there is not a single gene which is non-synonymously mutated more than once in CCPAP. (B) Copy number profile of CCPAP tumor sample 284. (C) The fraction of copy-number altered genome from CCPAP tumors profiled by both MSKCC and TCGA, as well as ccRCC tumors profiled by TCGA. (D) The relative mtDNA copy number in CCPAP and adjacent-normal tissues that were sequenced by either whole-exome-sequencing or whole-genome-sequencing. (E) Somatic variants identified by ultra-deep targeted duplex sequencing of mtDNA of CCPAP tumors.

Figure 4—source data 1

Mutation annotation file.

Figure 4—figure supplement 1
Mitochondrial gene expression analysis of CCPAP tumors.

(A) Differential expression analysis of nuclear-DNA-encoded mitochondrial genes. (B) Relative abundance of transcription of 13 mitochondrial genomes-encoded genes of CCPAP, CCRCC and matching normal tissues.

Figure 5 with 3 supplements
Immunohistochemical and histological characterization of CCPAP tumors.

(A) Representative IHC staining of mtDNA-encoded MT-CO1 and nuclear-DNA-encoded/mitochondrially-localized TOM20 of CCPAP, ccRCC tumors and adjacent normal tissues. (B) H-scores of immunohistochemical (IHC) staining for the mtDNA-encoded MT-CO1 protein across different subtypes of kidney cancers (pRCC, papillary RCC; chRCC, chromophobe RCC) **, p<0.01, ***, p<0.001, ****, p<0.0001, t-test. (CCPAP, n = 11; ccRCC, n = 7; pRCC, n = 2; chRCC, n = 4; oncocytoma, n = 4) (C) Representative IHC staining of 8-oxo-dG of CCPAP and ccRCC tumors. (D) H-score of IHC staining for 8-oxo-dG across different subtypes of kidney cancers. *, p<0.05, t-test. (CCPAP, n = 14; ccRCC, n = 3; pRCC, n = 3; oncocytoma, n = 2) (E) Representative images showing the CCPAP and ccRCC tumor regions stained with Oil-Red-O, Periodic acid–Schiff (PAS), and PAS diastase (n = 3 for each staining).

Figure 5—source data 1

Score for MT-CO1 IHC and 8-oxo-dG IHC.

Figure 5—figure supplement 1
MicroRNA and methylation analysis of CCPAP tumors.

(A) Plot of Adjusted p-value and relative abundance of miRNA (comparing CCPAP and CCRCC). (B) Principal components analysis of 450 k methylation data from the KIRC TCGA project. Green dots indicate ccRCC tumors, blue dots indicate adjacent normal tissue, and red dots indicate the 4 CCPAP samples. CCPAP tumors primarily cluster with normal tissue.

Figure 5—figure supplement 2
Mutation signature analysis of CCPAP tumor samples.
Figure 5—figure supplement 3
Heatmaps showing intersample correlations (red, positive; blue, negative) between profiles of 5 CCPAP tumors profiled by TCGA as well as average ccRCC (KIRC), chRCC (KICH) and pRCC(KIRP) (rows) and profiles of human and mouse kidney nephron sites (column).

Glom, kidney glomerulus; S1/S3, kidney proximal tubule; mTAL, kidney medullary thick ascending limb of Henle’s loop; cTAL, kidney cortical thick ascending limb of Henle’s loop; DCT, kidney distal convoluted tubule.

Author response image 1
Sorbitol Labeling from Glucose or Fructose in Mock Condition (A), Antimycin A Condition (B), and Rho-Zero Condition (C).

Left panel indicates total ion count. Right panel indicates labeling proportions, corrected for abundance of natural isotopes. Leftmost data of each panel is glucose labeling, rightmost data of each panel is fructose labeling.


Key resources table
Reagent type
(species) or
DesignationSource or referenceIdentifiersAdditional
AntibodyRabbit Anti-
TOM20 Antibody
Santa Cruz
Cat# sc-11415,
IHC (1:100)
AntibodyMouse Anti-
MTCO1 Antibody
AbcamCat# ab14705,
IHC (1:2000)
AntibodyMouse Anti-8-
Oxo-dG Antibody
Cat# MOG-020P,
IHC (1:400)

Data availability

The new data generated in this study is primarily tumor/germline sequencing of primary human tumor specimens, and constitutes human subject data. To protect the privacy of the human subjects, we have included somatic mutation calls in Figure 4 Source Data 1, but have withheld germline information. Source data have been provided for Figures 1-5. Controlled access for TCGA sequencing data (RNA-sequencing and whole exome sequencing of CCPAP tumors) are available via GDC commons data portal (https://gdc.cancer.gov/) by querying the 5 CCPAP sample IDs (BP-4760, BP-4784, BP-4795, DV-5567, BP-4177). Data from the The Cancer Genome Atlas Pan-Cancer Analysis Project related to this studied can be downloaded directly from firebrowse.org at the url http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/KIPAN/20160128/.

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