Abstract B37: Proteomic and metabolomic analyses of glioblastoma using mass spectrometry.
Authors: Ha Soo Jung , Showalter Gordon , Rickus Jenna , Cai Shanbao , Wang Haiyan , Liu Wei Michael , Sarkaria Jann N. , Springer John , Adamec Jiri , Pollok Karen E. , Clase Kari L. 
Published: August 2014

Glioblastoma (GBM) is the most common and malignant form of primary brain tumor. Average survival rate of GBM is 12-15 months from diagnosis. Due to its highly invasive nature, current treatment options have not been able to improve the survival rate in past 20 years. In order to discover GBM therapeutic targets, omics technologies have been widely used in order to identify potential biomarkers. Identification of cancer biomarkers for diagnosis and prognosis continues to evolve as clinical specimen pipelines and technologies are optimized. This research study focused on investigating proteins, metabolites and lipids biomarkers and patterns of human GBM xenografts employing mass spectrometry. Human tumor cell lines GBM 10 and GBM 43 were injected in the right cerebral hemisphere and flank sites in NOD/SCID mice (n = 10 mice per group). After harvesting brain and flank tumors and control brain from mouse models, protein, metabolites, and lipids of tumor samples were collected through one simple extraction procedure, which may solve sample preparation difficulties in omics studies, developed by Dr. Jiri Adamec. These samples were analyzed by reverse phase high performance liquid chromatography - Fourier transform ion cyclotron resonance mass spectrometry (LC-FTMS). FTMS analyzers have the highest resolving power of all MS instruments, which is ideal for complex mixtures such as GBM tissue. Spectra obtained from the FTMS analysis were analyzed using Student t-tests to detect significant differences in tissue profiles at a level of p = 0.05. Compounds below this threshold were identified through a database using the m/z ratio. Lipidomic analysis indicated the possible up and down-regulation of several lipids classes in GBM tissue connected to metabolic pathways, tumor proliferation and immunodepression, such as glycerophospholipids, both up- and down-regulated (e.g. PC38:4 or PG 35:2, respectively), and down-regulated glycosphingolipids (Galβ1-3GlcNAcβ1-3Galβ1-4Glc-Cer, Galβ1-4GlcNAcβ1-3Galβ1-4Glc-Cer), and triglycerides (TG 55:9, 53:4,52:8). We expect that integrated studies of proteomics, metabolomics, and lipidomics increase the confidence of identification of potential biomarkers. Accompanying these studies is a collaborative effort to improve the effectiveness and efficiency of computational pipelines that are imperative to the analytics, visualization, identification, and interpretation of the omics data. Only by carefully integrating the computational pipelines can we successfully perform the types of integrative studies needed to advance the identification of cancer biomarkers for diagnosis and prognosis, and our integrative studies serve as a case study for our pipeline advancement efforts.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B37.

Citation Format: Soo Jung Ha, Gordon Showalter, Jenna Rickus, Shanbao Cai, Haiyan Wang, Wei Michael Liu, Jann N. Sarkaria, John Springer, Jiri Adamec, Karen E. Pollok, Kari L. Clase. Proteomic and metabolomic analyses of glioblastoma using mass spectrometry. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B37.