Glioblastoma multiforme (GBM) is the most common and malignant form of primary brain tumors. It is highly invasive and current treatment options have not improved the survival rate over the past twenty years. Novel approaches and technologies from systems biology have the potential to identify biomarkers that could serve as new therapeutic targets for GBM. This study employed
AUTHORS' CONTRIBUTIONSKari Clase was responsible for leading the collaboration, analyzing data and interpreting findings within the context of the biological system and establishing the data collection and sample analysis pipeline. Soo Jung Ha and Gordon M. Showalter conducted the sample preparation, mass spectrometry data collection and statistical data analysis. Jiri Adamec supervised mass spectrometry data collection. John Springer conducted statistical clustering analysis of mass spectrometry data. Shanbao Cai, Haiyan Wang, Wei M. Liu were directly responsible for intellectual input on model set up. They also set up all flank and orthotopic model, mouse monitoring, and isolation of tumor samples. Karen Pollok was responsible regulatory requirements, intellectual input, and all aspects of in vivo model set up, monitoring, and sample collection. Jenna Rickus provided intellectual input. Jann Sarkaria provided tumor cell lines and intellectual input. Aaron Cohen-Gadol provided intellectual input.
CONFLICT OF INTERESTThe authors confirm that there is no conflict of interest with the data presented in this article. lipid profiling technology to investigate lipid biomarkers in ectopic and orthotopic human GBM xenograft models. Primary patient cell lines, GBM10 and GBM43, were injected into the flank and the right cerebral hemisphere of NOD/SCID mice. Tumors were harvested from the brain and flank and proteins, metabolites, and lipids extracted from each sample. Reverse phase based high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry (LC-FTMS) was used to analyze the lipid profiles of tumor samples. Statistical and clustering analyses were performed to detect differences. Over 500 lipids were identified in each tumor model and lipids with the greatest fold effect in the comparison of ectopic versus orthotopic tumor models fell predominantly into four main classes of lipids: glycosphingolipids, glycerophoshpoethanolamines, triradylglycerols, and glycerophosphoserines. Lipidomic analysis revealed differences in glycosphingolipid and triglyceride profiles when the same tumor was propagated in the flank versus the brain. These results underscore the importance of the surrounding physiological environment on tumor development and are consistent with the hypothesis that specific classes of lipids are critical for GBM tumor growth in different anatomical sites.
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