TY - JOUR TI - Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice AU - Kishimoto, Shun 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 A2 - DeBerardinis, Ralph A2 - Akhmanova, Anna A2 - DeBerardinis, Ralph A2 - Brindle, Kevin VL - 8 PY - 2019 DA - 2019/08/13 SP - e46312 C1 - eLife 2019;8:e46312 DO - 10.7554/eLife.46312 UR - https://doi.org/10.7554/eLife.46312 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 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. KW - imaging KW - MRI KW - metabolism KW - metabolomics KW - tumor microenvironment KW - magnetic resonance spectroscopy JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -