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

Shun Kishimoto; Jeffrey R. Brender; Daniel R. Crooks; Shingo Matsumoto; Tomohiro Seki; Nobu Oshima; Hellmut Merkle; Penghui Lin; Galen Reed; Albert P. Chen; Jan Henrik Ardenkjaer-Larsen; Jeeva Munasinghe; Keita Saito; Kazutoshi Yamamoto; Peter L. Choyke; James Mitchell; Andrew N. Lane; Teresa Wm Fan; W. Marston Linehan; Murali C. Krishna

doi:10.7554/eLife.46312

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

Shun Kishimoto, Jeffrey R. Brender, Daniel R. Crooks, Shingo Matsumoto, Tomohiro Seki, Nobu Oshima, Hellmut Merkle, Penghui Lin, Galen Reed, Albert P. Chen, Jan Henrik Ardenkjaer-Larsen, Jeeva Munasinghe, Keita Saito, Kazutoshi Yamamoto, Peter L. Choyke, James Mitchell, Andrew N. Lane, Teresa Wm Fan, W. Marston Linehan, Murali C. Krishna

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

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 by 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 from¹³C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

Original language	English
Article number	e46312
Journal	eLife
Volume	8
DOIs	https://doi.org/10.7554/eLife.46312
State	Published - Aug 2019

Bibliographical note

Publisher Copyright:
Ó This is an open-access article.

ASJC Scopus subject areas

General Neuroscience
General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.7554/eLife.46312

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Kishimoto, S., Brender, J. R., Crooks, D. R., Matsumoto, S., Seki, T., Oshima, N., Merkle, H., Lin, P., Reed, G., Chen, A. P., Ardenkjaer-Larsen, J. H., Munasinghe, J., Saito, K., Yamamoto, K., Choyke, P. L., Mitchell, J., Lane, A. N., Fan, T. W., Linehan, W. M., & Krishna, M. C. (2019). Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice. eLife, 8, Article e46312. https://doi.org/10.7554/eLife.46312

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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 by 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 from13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.",

author = "Shun Kishimoto and Brender, {Jeffrey R.} and Crooks, {Daniel R.} and Shingo Matsumoto and Tomohiro Seki and Nobu Oshima and Hellmut Merkle and Penghui Lin and Galen Reed and Chen, {Albert P.} and Ardenkjaer-Larsen, {Jan Henrik} and Jeeva Munasinghe and Keita Saito and Kazutoshi Yamamoto and Choyke, {Peter L.} and James Mitchell and Lane, {Andrew N.} and Fan, {Teresa Wm} and Linehan, {W. Marston} and Krishna, {Murali C.}",

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Kishimoto, S, Brender, JR, Crooks, DR, Matsumoto, S, Seki, T, Oshima, N, Merkle, H, Lin, P, Reed, G, Chen, AP, Ardenkjaer-Larsen, JH, Munasinghe, J, Saito, K, Yamamoto, K, Choyke, PL, Mitchell, J, Lane, AN , Fan, TW, Linehan, WM & Krishna, MC 2019, 'Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice', eLife, vol. 8, e46312. https://doi.org/10.7554/eLife.46312

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AU - Brender, Jeffrey R.

AU - Crooks, Daniel R.

AU - Matsumoto, Shingo

AU - Seki, Tomohiro

AU - Oshima, Nobu

AU - Merkle, Hellmut

AU - Lin, Penghui

AU - Reed, Galen

AU - Chen, Albert P.

AU - Ardenkjaer-Larsen, Jan Henrik

AU - Munasinghe, Jeeva

AU - Saito, Keita

AU - Yamamoto, Kazutoshi

AU - Choyke, Peter L.

AU - Mitchell, James

AU - Lane, Andrew N.

AU - Fan, Teresa Wm

AU - Linehan, W. Marston

AU - Krishna, Murali C.

N1 - Publisher Copyright: Ó This is an open-access article.

PY - 2019/8

Y1 - 2019/8

N2 - 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 by 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 from13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

AB - 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 by 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 from13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

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

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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