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.

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.

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.

Metrics

  • 3,544
    views
  • 429
    downloads
  • 23
    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. 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

Further reading

    1. Cancer Biology
    Qianqian Ju, Wenjing Sheng ... Cheng Sun
    Research Article

    TAK1 is a serine/threonine protein kinase that is a key regulator in a wide variety of cellular processes. However, the functions and mechanisms involved in cancer metastasis are still not well understood. Here, we found that TAK1 knockdown promoted esophageal squamous cancer carcinoma (ESCC) migration and invasion, whereas TAK1 overexpression resulted in the opposite outcome. These in vitro findings were recapitulated in vivo in a xenograft metastatic mouse model. Mechanistically, co-immunoprecipitation and mass spectrometry demonstrated that TAK1 interacted with phospholipase C epsilon 1 (PLCE1) and phosphorylated PLCE1 at serine 1060 (S1060). Functional studies revealed that phosphorylation at S1060 in PLCE1 resulted in decreased enzyme activity, leading to the repression of phosphatidylinositol 4,5-bisphosphate (PIP2) hydrolysis. As a result, the degradation products of PIP2 including diacylglycerol (DAG) and inositol IP3 were reduced, which thereby suppressed signal transduction in the axis of PKC/GSK-3β/β-Catenin. Consequently, expression of cancer metastasis-related genes was impeded by TAK1. Overall, our data indicate that TAK1 plays a negative role in ESCC metastasis, which depends on the TAK1-induced phosphorylation of PLCE1 at S1060.

    1. Cancer Biology
    2. Cell Biology
    Xiangning Bu, Nathanael Ashby ... Inhee Chung
    Research Article

    Cell crowding is a common microenvironmental factor influencing various disease processes, but its role in promoting cell invasiveness remains unclear. This study investigates the biomechanical changes induced by cell crowding, focusing on pro-invasive cell volume reduction in ductal carcinoma in situ (DCIS). Crowding specifically enhanced invasiveness in high-grade DCIS cells through significant volume reduction compared to hyperplasia-mimicking or normal cells. Mass spectrometry revealed that crowding selectively relocated ion channels, including TRPV4, to the plasma membrane in high-grade DCIS cells. TRPV4 inhibition triggered by crowding decreased intracellular calcium levels, reduced cell volume, and increased invasion and motility. During this process, TRPV4 membrane relocation primed the channel for later activation, compensating for calcium loss. Analyses of patient-derived breast cancer tissues confirmed that plasma membrane-associated TRPV4 is specific to high-grade DCIS and indicates the presence of a pro-invasive cell volume reduction mechanotransduction pathway. Hyperosmotic conditions and pharmacologic TRPV4 inhibition mimicked crowding-induced effects, while TRPV4 activation reversed them. Silencing TRPV4 diminished mechanotransduction in high-grade DCIS cells, reducing calcium depletion, volume reduction, and motility. This study uncovers a novel pro-invasive mechanotransduction pathway driven by cell crowding and identifies TRPV4 as a potential biomarker for predicting invasion risk in DCIS patients.