UNLABELLED: The aim of this study was to characterize the different phenotypes of osteosarcoma by PET, comparing the uptake of 3 tracers ((18)F-FDG, (18)F-fluoromisonidazole [(18)F-FMISO], and (18)F-fluoride) in preclinical mouse models that reflect the heterogeneity of the human disease. METHODS: Mouse LM8 osteosarcoma, human 143B, and Caprin-1 stably overexpressing SaOS-2 cells were injected intratibially in C3H and severe-combined immunodeficient mice. PET imaging with (18)F-FDG, (18)F-FMISO, and (18)F-fluoride was performed in these mouse models, and a ratio between the standardized uptake value of the primary tumor and a control area of bone was calculated and compared among the models. Histology and immunohistochemistry were performed to confirm the PET findings. RESULTS: The pattern of tracer uptake differed among the primary tumors of the 3 models in accordance with the histology and immunohistochemistry on primary tumor sections. The osteolytic tumors in the 143B model showed the highest uptake of (18)F-FDG, an indicator of glucose metabolism, which was significantly higher (P < 0.05) than in the SaOS-2/Caprin-1 model and correlated with the percentage of Ki67-positive cells in the primary tumors. Hypoxia, indicated by (18)F-FMISO accumulation, was higher in the SaOS-2/Caprin-1 and 143B cell line-derived tumors (P < 0.01). Finally (18)F-fluoride, a marker of bone remodeling, correlated with the osteoblastic phenotype. The SaOS-2/Caprin-1 cell-derived tumors showed a significantly higher uptake than the moderately osteoblastic LM8 (P < 0.05) and the osteolytic 143B (P < 0.01) cell line-derived tumors. CONCLUSION: Differential PET imaging with tracers indicating metabolic activity, hypoxia, or bone remodeling will be helpful for the characterization of different osteosarcoma phenotypes and subsequent evaluation of more specific treatment modalities targeting the processes that are predominant in each specific tumor type or subtype.
UNLABELLED: The aim of this study was to characterize the different phenotypes of osteosarcoma by PET, comparing the uptake of 3 tracers ((18)F-FDG, (18)F-fluoromisonidazole [(18)F-FMISO], and (18)F-fluoride) in preclinical mouse models that reflect the heterogeneity of the human disease. METHODS:Mouse LM8 osteosarcoma, human 143B, and Caprin-1 stably overexpressing SaOS-2 cells were injected intratibially in C3H and severe-combined immunodeficientmice. PET imaging with (18)F-FDG, (18)F-FMISO, and (18)F-fluoride was performed in these mouse models, and a ratio between the standardized uptake value of the primary tumor and a control area of bone was calculated and compared among the models. Histology and immunohistochemistry were performed to confirm the PET findings. RESULTS: The pattern of tracer uptake differed among the primary tumors of the 3 models in accordance with the histology and immunohistochemistry on primary tumor sections. The osteolytic tumors in the 143B model showed the highest uptake of (18)F-FDG, an indicator of glucose metabolism, which was significantly higher (P < 0.05) than in the SaOS-2/Caprin-1 model and correlated with the percentage of Ki67-positive cells in the primary tumors. Hypoxia, indicated by (18)F-FMISO accumulation, was higher in the SaOS-2/Caprin-1 and 143B cell line-derived tumors (P < 0.01). Finally (18)F-fluoride, a marker of bone remodeling, correlated with the osteoblastic phenotype. The SaOS-2/Caprin-1 cell-derived tumors showed a significantly higher uptake than the moderately osteoblastic LM8 (P < 0.05) and the osteolytic 143B (P < 0.01) cell line-derived tumors. CONCLUSION: Differential PET imaging with tracers indicating metabolic activity, hypoxia, or bone remodeling will be helpful for the characterization of different osteosarcoma phenotypes and subsequent evaluation of more specific treatment modalities targeting the processes that are predominant in each specific tumor type or subtype.
Authors: James C Davis; Najat C Daw; Fariba Navid; Catherine A Billups; Jianrong Wu; Armita Bahrami; Jesse J Jenkins; Scott E Snyder; Wilburn E Reddick; Victor M Santana; M Beth McCarville; Junyu Guo; Barry L Shulkin Journal: J Nucl Med Date: 2017-06-13 Impact factor: 10.057
Authors: Sven De Bruycker; Christel Vangestel; Tim Van den Wyngaert; Leonie Wyffels; An Wouters; Patrick Pauwels; Steven Staelens; Sigrid Stroobants Journal: Mol Imaging Biol Date: 2016-08 Impact factor: 3.488
Authors: Olga Neklyudova; Matthias J E Arlt; Patrick Brennecke; Marcus Thelen; Ana Gvozdenovic; Aleksandar Kuzmanov; Bernhard Robl; Sander M Botter; Walter Born; Bruno Fuchs Journal: J Cancer Res Clin Oncol Date: 2016-06-14 Impact factor: 4.553
Authors: María Collantes; Naiara Martínez-Vélez; Marta Zalacain; Lucia Marrodán; Margarita Ecay; María José García-Velloso; Marta María Alonso; Ana Patiño-García; Iván Peñuelas Journal: BMC Cancer Date: 2018-11-29 Impact factor: 4.430
Authors: Sven De Bruycker; Christel Vangestel; Steven Staelens; Tim Van den Wyngaert; Sigrid Stroobants Journal: Contrast Media Mol Imaging Date: 2018-10-18 Impact factor: 3.161