Craig B. Thompson et. Al
Generally, cancer cells contain some metabolic mutation that favors rapid reproduction and the accumulation of biomass, and subsequently results in an increased uptake of both glutamine and glucose. In this paper, the authors further subcategorized cancer cells into primary glibolastomas and secondary glioblastomas. Primary glioblastomas tend to show deregulation of PI3K, which results in unbalanced growth factor throughout the cell. Secondary glioblastomas, on the other hand, tend to have mutations in Isocitrate dehydrogenase 1 (IDH1), which results in the accumulation of the oncometabolite 2-hydroxyglutarate (2-HG). The rapid uptake of glucose has traditionally been used to diagnose and quantify different cancers through the use of 18F-FDG PET imaging (where 18F-FDG is 18F-fluorodeoxygluocse, a fluorescing analog of glucose, and PET is positron emission tomography). This technique, however, is less effective at diagnosing cancer in the brain due to the high glucose metabolism naturally present in the brain and results in poor imaging and therefore poor diagnosis. Current imaging analogs other than 18F-FDG, such as amino acid analogs, while more effective at diagnosing brain tumors, are still incapable of indiscriminately determining the metabolic state of glioblastomas. Given the high fatality rate of “neurologically destructive gliomas”, it is clear more noninvasive forms of diagnosis that allow for greater characterization of the metabolic state of the tumor need to be developed.
The authors of this paper, hypothesized, that the rapid uptake of glutamine in cancer cells (cancers such as lymphoma and pancreatic adenocarcinoma use increased glutamine metabolism to support growth), but not in normal brain cells, might offer an avenue to achieve this new method of diagnosis. Previous to this paper, the authors developed a fluorescent analog to glutamine, which they labeled 18F-FGln (4-18F-(2S, 4R)-fluoroglutamine). In this paper, the authors showed that brain tumors demonstrated high uptake of the analog, whereas normal brain cells showed minimal uptake, which suggests that this molecule in combination with PET imaging can be used to effectively diagnose and distinguish between different brain cancers. The authors also demonstrated that 18F-FGln allowed for greater delineation and assessment of the metabolic uptake of gliomas, suggesting it may be used to more effectively determine the progression of glioblastomas.
So how did the authors go about proving their hypothesis? First, the authors proved that 18F-FGln was a viable substrate to use for PET imaging. To accomplish this, they compared the metabolism of glutamine with the metabolism of 19F-Gln (a more stable form of 18F-FGln, which allowed for mass spec analysis), and discovered that cells given normal glutamine produced glutamate, however, cells given 19F-Gln produced no 19F-glutamate. This signified to the authors that the fluorescence witnessed during PET imaging corresponds strongly to glutamine uptake and that 18F-FGln is trapped in gliomas after uptake by incorporation into protein. The authors then injected mice with with various types of glioma cells to create xenograft (the cells that are transferred from one organism to another) models. The authors then injected both the control mice and the xenograft models with 18F-FGln and 18F-FDG and used PET scans to generate images of the various brains. The figure below discusses some of their results. Part A of the figure simply shows the structure of 18F-FGln. Figure B shows the amount of 18F-FGln taken up by a normal brain as opposed to the xenograft models. Figure C shows the amount of 18F-FDG taken up by the normal brain as compared to the xenograft models. The take home message here is that the normal brain took up significantly less 18F-FGln compared to the xenograft models than 18F-FDG. Figures E, F, and G are PET scans of mice with normal brains, and two different xenograft models, respectively. Figure E demonstrates that there is clearly more background uptake of 18F-FDG than 18F-FGln. Figures F and G amplify this point, as there is clearly much higher tumor resolution in the mice injected with 18F-FGln as opposed to the mice injected with 18F-FDG, as indicated by the red arrows and the defined level of substrate uptake (red being the highest) in 18F-FGln injected mice.
In the next section of the paper, the authors attempted to address any factors that might affect the quality of the image produced. Because 18F-FDG PET scans have historically given poor scans during inflammation, the authors created “robust neuroinflammation” within mice models by injecting the mice with immune stimulating macrophages and interferon-γ (both of which result in an inflammatory phenotype). The authors then produced PET scans using both 18F-FGln and 18F-FDG, and determined that while there was increased background uptake of 18F-FDG during inflammation, there was no increased background uptake of 18F-FGln during inflammation. As increased background uptake results in poorer resolution, this data suggests that 18F-FGln might surpass 18F-FDG as a diagnostic tool in the presence of inflammation, especially in the brain.
In their last sections of the paper, the authors correlated 18F-FGln uptake with the metabolic stage and robustness of tumors. The authors demonstrated that a mouse model treated showed significantly less uptake of 18F-FGln after treated with chemotherapy (this was confirmed by PET scans and MRI imaging). The authors’ subsequent study might be their most significant, as they shifted their focus to human trials. First, the authors determined using siRNA knockdown and pharmacological inhibition that increased expression of SLC1A5 (a glutamine transporter) in gliomas as compared to normal brain tissue correlated to increased uptake of 18F-FGln, suggesting to the authors that the substrate might have wider applications in characterizing gliomas than just the genotypes tested. Finally, the authors demonstrated that 18F-FGln results in high uptake in progressive tumors in humans, and can potentially be used to help identify transforming tumors.
This paper is interesting in terms of significance as it seems the authors are attempting to occupy a niche among all the other imaging analogs that have been released and used in the past few years. The significance, from the authors standpoint, is that with more development and research, hopefully 18F-FGln can confidently discriminate between various stages of cancer, and provide the doctor with an accurate metabolic characterization of the glioblastoma. Obviously correct and encompassing characterization of cancer is essential for timely diagnosis and treatment, so in that respect, if the authors eventually succeed in proving that this analog can without a doubt determine the metabolic state of gliomas, then this analog might fortify its niche within the field of cancer diagnosis.
By the way, I could not figure out how to hyperlink in the comments, so here are the links to the papers I cited in the comments: