Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice

  1. Shun Kishimoto
  2. Jeffrey R Brender  Is a corresponding author
  3. Daniel R Crooks
  4. Shingo Matsumoto
  5. Tomohiro Seki
  6. Nobu Oshima
  7. Hellmut Merkle
  8. Penghui Lin
  9. Galen Reed
  10. Albert P Chen
  11. Jan Henrik Ardenkjaer-Larsen
  12. Jeeva Munasinghe
  13. Keita Saito
  14. Kazutoshi Yamamoto
  15. Peter L Choyke
  16. James Mitchell
  17. Andrew N Lane
  18. Teresa Fan
  19. W Marston Linehan
  20. Murali C Krishna  Is a corresponding author
  1. National Cancer Institute, National Institutes of Health, United States
  2. Hokkaido University, Japan
  3. National Institute of Neurological Disorders and Stroke, National Institutes of Health, United States
  4. University of Kentucky, United States
  5. GE Healthcare, Canada

Abstract

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity was not detectable in FDG-PET.

Data availability

Glucose imaging data and related files have been deposited to Dataverse at https://doi.org/10.7910/DVN/XU9XH9

Article and author information

Author details

  1. Shun Kishimoto

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  2. Jeffrey R Brender

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    For correspondence
    cherukum@mail.nih.gov
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7487-6169
  3. Daniel R Crooks

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  4. Shingo Matsumoto

    Graduate School of Information Science and Technology, Division of Bioengineering and Bioinformatics, Hokkaido University, Sapporo, Japan
    Competing interests
    No competing interests declared.
  5. Tomohiro Seki

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  6. Nobu Oshima

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  7. Hellmut Merkle

    National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  8. Penghui Lin

    Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    Competing interests
    No competing interests declared.
  9. Galen Reed

    Research Circle Technology, GE Healthcare, Toronto, Canada
    Competing interests
    Galen Reed, is affiliated with GE HealthCare. The author has no other competing interests to declare..
  10. Albert P Chen

    Research Circle Technology, GE Healthcare, Toronto, Canada
    Competing interests
    Albert P Chen, is affiliated with GE HealthCare. The author has no other competing interests to declare..
  11. Jan Henrik Ardenkjaer-Larsen

    Research Circle Technology, GE Healthcare, Toronto, Canada
    Competing interests
    Jan Henrik Ardenkjaer-Larsen, is affiliated with GE HealthCare. The author has no other competing interests to declare..
  12. Jeeva Munasinghe

    In Vivo NMR Center, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  13. Keita Saito

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  14. Kazutoshi Yamamoto

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  15. Peter L Choyke

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  16. James Mitchell

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  17. Andrew N Lane

    Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    Competing interests
    No competing interests declared.
  18. Teresa Fan

    Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    Competing interests
    No competing interests declared.
  19. W Marston Linehan

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  20. Murali C Krishna

    Radiation Biology Branch, National Cancer Institute, National Institutes of Health, Bethesda, United States
    For correspondence
    murali@helix.nih.gov
    Competing interests
    No competing interests declared.

Funding

National Cancer Institute (1ZIASC006321-39)

  • James Mitchell

National Cancer Institute (Intramural Research Program)

  • Murali C Krishna

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

Reviewing Editor

  1. Ralph DeBerardinis, UT Southwestern Medical Center, United States

Ethics

Animal experimentation: The animal experiments were conducted according to a protocol approved by the Animal Research Advisory Committee of the NIH (RBB-159-2SA) in accordance with the National Institutes of Health Guidelines for Animal Research.

Version history

  1. Received: February 23, 2019
  2. Accepted: August 8, 2019
  3. Accepted Manuscript published: August 13, 2019 (version 1)
  4. Version of Record published: August 22, 2019 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Shun Kishimoto
  2. Jeffrey R Brender
  3. Daniel R Crooks
  4. Shingo Matsumoto
  5. Tomohiro Seki
  6. Nobu Oshima
  7. Hellmut Merkle
  8. Penghui Lin
  9. Galen Reed
  10. Albert P Chen
  11. Jan Henrik Ardenkjaer-Larsen
  12. Jeeva Munasinghe
  13. Keita Saito
  14. Kazutoshi Yamamoto
  15. Peter L Choyke
  16. James Mitchell
  17. Andrew N Lane
  18. Teresa Fan
  19. W Marston Linehan
  20. Murali C Krishna
(2019)
Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice
eLife 8:e46312.
https://doi.org/10.7554/eLife.46312

Share this article

https://doi.org/10.7554/eLife.46312

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