The impact of gene expression on 18F-FDG kinetics; a new chapter for diagnostic nuclear medicine

Nuclear medicine procedures are the methods of choice for the assessment of the tracer kinetics in a volume over time. Fluorine-18 fluoro-deoxyglucose ((18)F-FDG) is primarily a marker of tumor viability and the kinetics of (18)F-FDG reflects major biological factors like angiogenesis and proliferation. The correct interpretation of (18)F- FDG tracer kinetics demands the knowledge about the association of quantitative positron emission tomography (PET) data and gene expression. The use of gene arrays is helpful to obtain expression data for a large number of genes from tissue samples. However, limited data are available about quantitative (18)F-FDG data and gene array results. Studies in primary liver cancer patients revealed that the (18)F-FDG uptake was associated with genes related to tumor cell adhesion and tumor invasion. We noted in patients with giant cell tumors a correlation of the (18)F-FDG uptake, as measured by the standardized uptake value (SUV) and the cell division cycle 2 (cdc2) gene expression. The effect of therapeutic interventions is dependent on the agent used for treatment. In gastrointestinal stromal tumors the change in (18)F-FDG uptake is most likely due to an antiproliferative effect. However, this may be different in other tumor types and for other treatment protocols, therefore dedicated studies of the (18)F-FDG kinetics and gene expression are needed. Based on the recent data available in colorectal tumors and gene expression, we were able to demonstrate that at least two key genes of the angiogenesis, vascular endothelial growth factor (VEGF-A) and angiopoietin-2, have a major impact on the tracer kinetics. Furthermore, regression functions for the (18)F-FDG kinetics and gene expression data facilitate the calculation of parametric images of the gene expression, reflecting the spatial distribution of angiogenesis in a colorectal tumor. Currently the development of information management systems for the prediction of clinical relevant information in individual patients is in progress to retrieve the optimum on information from individual (18)F-FDG patient examinations to support individualization of treatment management.