Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Daniel R Crooks
Urologic Oncology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Shingo Matsumoto
Graduate School of Information Science and Technology, Division of Bioengineering and Bioinformatics, Hokkaido University, Sapporo, Japan; JST, PREST, Saitama, Japan
Tomohiro Seki
Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Nobu Oshima
Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Hellmut Merkle
NINDS, NIH, Bethesda, United States
Penghui Lin
Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, United States
Galen Reed
GE HealthCare, Chicago, United States
Albert P Chen
GE HealthCare, Chicago, United States
Jan Henrik Ardenkjaer-Larsen
GE HealthCare, Chicago, United States; Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Jeeva Munasinghe
NINDS, NIH, Bethesda, United States
Keita Saito
Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Kazutoshi Yamamoto
Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Peter L Choyke
Molecular Imaging Program, Center for Cancer Research, NCI, NIH, Bethesda, United States
James Mitchell
Radiation Biology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
Andrew N Lane
Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, United States; Markey Cancer Center, University of Kentucky, Lexington, United States
Teresa WM Fan
Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, United States; Markey Cancer Center, University of Kentucky, Lexington, United States
W Marston Linehan
Urologic Oncology Branch, Center for Cancer Research, NCI, NIH, Bethesda, United States
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 from 13C-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.