Glioma-derived stem-like cells (GSCs): Quantitative Proteomics

Glioma-derived stem-like cells (GSCs) are hypothesized to provide a repository of cells in tumors that can self-replicate and be resistant to radiation and chemotherapeutic agents developed for the treatment of tumors. The potential lack of response of GSCs to traditional cytotoxic and radiation therapies has significant implications for tumor biology and therapeutics. At M.D. Anderson Cancer Center, a large number of GSC lines representing several glioma sub-types have been isolated and characterized with respect to gene expression. In order to identify potential therapeutic targets relating to GSC phenotype, we examined protein expression in 35 GSC lines relative to an external protein standard using label-free quantitative proteomics. Samples were analyzed in triplicate by nanoLC-MS/MS (Orbitrap Elite, Thermo) in block-randomized groups of three GSCs plus the external standard. The resulting .raw files were aligned by group in Progenesis LC-MS (Nonlinear Dynamics). Database searching was performed with PEAKS 6 software (BSI) against a UniprotKB/SwissProt Human database appended with the cRAP contaminant database. Peptide intensities were exported for analysis in DanteR. The resulting relative fold changes were used to drive unsupervised hierarchical clustering of the cell lines for the purpose of classifying cell lines, and Ingenuity Pathway Analysis (IPA) and the DAVID webtool were used to determine the biochemical pathways and biological processes impacted by protein changes in each cell line. Within IPA, Upstream Analysis was used to predict biological functions and transcriptional regulators whose increased or decreased activity was consistent with the observed fold changes, and the resulting upstream regulators with significant z-scores were exported for unsupervised hierarchical clustering. In addition to showing upstream regulators known to be associated with glioma, including Myc, tumor protein 53 (TP53), N-myc proto-oncogene protein (MYCN), two novel upstream regulators, synoviolin 1 (SYVN1) and interleukin 5 (IL5) were identified. A cluster of proteins found to be decreased relative to mixed control in mesenchymal cell lines was subjected to further bioinformatics analysis, identifying SRSF2 as a potential upstream regulator.