Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma

  1. Jianing Xu
  2. Eduard 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 G Hoekstra
  11. Li Zhang
  12. Peter Sajjakulnukit
  13. Daniel Kremer
  14. Zachary P Tolstyka
  15. Jozefina Casuscelli
  16. Steve Stirdivant
  17. Jie Tang
  18. Nikolaus Schultz
  19. Paul S Jeng
  20. Yiyu Dong
  21. Wenjing Su
  22. Emily H-Y 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 Lyssiotis  Is a corresponding author
  32. Satish K Tickoo  Is a corresponding author
  33. Abraham Ari Hakimi  Is a corresponding author
  1. Memorial Sloan Kettering Cancer Center, United States
  2. University of Michigan, United States
  3. Baylor College of Medicine, United States
  4. University of Washington, United States
  5. Ludwig-Maximilians University, Germany
  6. Metabolon Inc, United States
  7. Cedars-Sinai Medical Center, United States
  8. Dana-Farber Cancer Institute, United States
  9. Washington University in St Louis, United States

Abstract

While genomic sequencing routinely identifies oncogenic alterations for the majority of cancers, many tumors harbor no discernable driver lesion. Here, we describe the exceptional molecular phenotype of a genomically quiet kidney tumor, clear cell papillary renal cell carcinoma (CCPAP). In spite of a largely wild-type nuclear genome, CCPAP tumors exhibit severe depletion of mitochondrial DNA (mtDNA) and RNA and high levels of oxidative stress, reflecting a shift away from respiratory metabolism. Moreover, CCPAP tumors exhibit a distinct metabolic phenotype uniquely characterized by accumulation of the sugar alcohol sorbitol. Immunohistochemical staining of primary CCPAP tumor specimens recapitulates both the depletion of mtDNA-encoded proteins and a lipid-depleted metabolic phenotype, suggesting that the cytoplasmic clarity in CCPAP is primarily related to the presence of glycogen. These results argue for non-genetic profiling as a tool for the study of cancers of unknown driver.

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/.

Article and author information

Author details

  1. Jianing Xu

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  2. Eduard Reznik

    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States
    For correspondence
    reznike@mskcc.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6511-5947
  3. Ho-Joon Lee

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    Competing interests
    No competing interests declared.
  4. Gunes Gundem

    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  5. Philip Jonsson

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  6. Judy Sarungbam

    Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  7. Anna Bialik

    Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  8. Francisco Sanchez-Vega

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  9. Chad J Creighton

    Department of Medicine, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  10. Jake G Hoekstra

    Department of Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  11. Li Zhang

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    Competing interests
    No competing interests declared.
  12. Peter Sajjakulnukit

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    Competing interests
    No competing interests declared.
  13. Daniel Kremer

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    Competing interests
    No competing interests declared.
  14. Zachary P Tolstyka

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    Competing interests
    No competing interests declared.
  15. Jozefina Casuscelli

    Department of Urology, Ludwig-Maximilians University, Munich, Germany
    Competing interests
    No competing interests declared.
  16. Steve Stirdivant

    Metabolon Inc, Durham, United States
    Competing interests
    Steve Stirdivant, This author is a former employee of Metabolon, Inc..
  17. Jie Tang

    Genomics Core, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, United States
    Competing interests
    No competing interests declared.
  18. Nikolaus Schultz

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  19. Paul S Jeng

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  20. Yiyu Dong

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  21. Wenjing Su

    Molecular Pharmacology and Chemistry Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  22. Emily H-Y Cheng

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  23. Paul Russo

    Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  24. Jonathan A Coleman

    Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  25. Elli Papaemmanuil

    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  26. Ying-Bei Chen

    Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5207-3648
  27. Victor E Reuter

    Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
  28. Chris Sander

    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    No competing interests declared.
  29. Scott R Kennedy

    Department of Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  30. James J Hsieh

    Molecular Oncology, Department of Medicine, Siteman Cancer Center, Washington University in St Louis, St Louis, United States
    Competing interests
    No competing interests declared.
  31. Costas Lyssiotis

    Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, United States
    For correspondence
    clyssiot@med.umich.edu
    Competing interests
    No competing interests declared.
  32. Satish K Tickoo

    Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
    For correspondence
    tickoos@mskcc.org
    Competing interests
    No competing interests declared.
  33. Abraham Ari Hakimi

    Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    For correspondence
    hakimia@mskcc.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0930-8824

Funding

Damon Runyon Cancer Research Foundation (Dale F. Frey Award for Breakthrough Scientists DFS-09-14)

  • Costas Lyssiotis

Sidney Kimmel Foundation for Cancer Research (Sidney Kimmel Center for Prostate and Urologic Cancers)

  • Abraham Ari Hakimi

American Urological Association (Research Scholar Award)

  • Abraham Ari Hakimi

V Foundation for Cancer Research (Junior Scholar Award V2016-009)

  • Costas Lyssiotis

Sidney Kimmel Foundation for Cancer Research (Kimmel Scholar Award SKF-16-005)

  • Costas Lyssiotis

National Institutes of Health (DK097153)

  • Costas Lyssiotis

Charles Woodson Research Fund

  • Costas Lyssiotis

the UM Pediatric Brain Tumor Initiative

  • Costas Lyssiotis

University of Michigan's Program in Chemical Biology (Graduate Assistance in Areas of National Need (GAANN) award)

  • Daniel Kremer

National Cancer Institute (P30 CA008748)

  • Jianing Xu
  • Eduard Reznik
  • Gunes Gundem
  • Philip Jonsson
  • Judy Sarungbam
  • Anna Bialik
  • Francisco Sanchez-Vega
  • Jozefina Casuscelli
  • Nikolaus Schultz
  • Yiyu Dong
  • Paul Russo
  • Jonathan A Coleman
  • Elli Papaemmanuil
  • Ying-Bei Chen
  • Victor E Reuter
  • Chris Sander
  • Satish K Tickoo
  • Abraham Ari Hakimi

National Cancer Institute (P30 CA046592)

  • Costas Lyssiotis

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Frozen samples and genomic data were acquired through MSKCC IRB approved tissue protocol 06-107.

Copyright

© 2019, Xu et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,516
    views
  • 336
    downloads
  • 37
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jianing Xu
  2. Eduard Reznik
  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 G Hoekstra
  11. Li Zhang
  12. Peter Sajjakulnukit
  13. Daniel Kremer
  14. Zachary P Tolstyka
  15. Jozefina Casuscelli
  16. Steve Stirdivant
  17. Jie Tang
  18. Nikolaus Schultz
  19. Paul S Jeng
  20. Yiyu Dong
  21. Wenjing Su
  22. Emily H-Y 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 Lyssiotis
  32. Satish K Tickoo
  33. Abraham Ari Hakimi
(2019)
Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma
eLife 8:e38986.
https://doi.org/10.7554/eLife.38986

Share this article

https://doi.org/10.7554/eLife.38986

Further reading

    1. Cancer Biology
    2. Evolutionary Biology
    Arman Angaji, Michel Owusu ... Johannes Berg
    Research Article

    In growing cell populations such as tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.

    1. Cancer Biology
    2. Evolutionary Biology
    Susanne Tilk, Judith Frydman ... Dmitri A Petrov
    Research Article

    In asexual populations that don’t undergo recombination, such as cancer, deleterious mutations are expected to accrue readily due to genome-wide linkage between mutations. Despite this mutational load of often thousands of deleterious mutations, many tumors thrive. How tumors survive the damaging consequences of this mutational load is not well understood. Here, we investigate the functional consequences of mutational load in 10,295 human tumors by quantifying their phenotypic response through changes in gene expression. Using a generalized linear mixed model (GLMM), we find that high mutational load tumors up-regulate proteostasis machinery related to the mitigation and prevention of protein misfolding. We replicate these expression responses in cancer cell lines and show that the viability in high mutational load cancer cells is strongly dependent on complexes that degrade and refold proteins. This indicates that the upregulation of proteostasis machinery is causally important for high mutational burden tumors and uncovers new therapeutic vulnerabilities.