Shun Kishimoto 1 , Jeffrey R Brender , 1 , Daniel R Crooks 2 , Shingo Matsumoto 3 , 4 , Tomohiro Seki 1 , Nobu Oshima 1 , Hellmut Merkle 5 , Penghui Lin 6 , Galen Reed 7 , Albert P Chen 7 , Jan Henrik Ardenkjaer-Larsen 7 , 8 , Jeeva Munasinghe 5 , Keita Saito 1 , Kazutoshi Yamamoto 1 , Peter L Choyke 9 , James Mitchell 1 , Andrew N Lane 6 , 10 , Teresa WM Fan 6 , 10 , W Marston Linehan 2 , Murali C Krishna , 1
13 August 2019
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.