Glioblastoma (GMB) is the most aggressive type of brain cancer and represents 15% of all forms diagnosed. Symptoms are initially non-specific but progress rapidly. GMB’s exhibit aggressive growth, limited response to therapeutic intervention and high instance of reoccurrence. As the survival rate for patients is 12- 15 months after diagnosis, early detection is critical.
Marziali, G. et al identified two glioblastoma stem-like cell (GSC) clusters, a pro-neural-like phenotype and a mesenchymal-like phenotype, via gene expression profiling, NMR Spectroscopy metabolite profiling and analyte detected via RPPA endpoint analysis in patient lysate samples. Tumor identification based on RPPA metabolic/ proteomic profiling facilitated differentiation of the two GSC cluster subsets, ‘GSr’ and ‘GSf’, characterized by varying ratios of SRC, a proto-oncogene tyrosine protein kinase, and RPS6-ribosomal protein S6, a downstream effector of the mTOR pathway. Of the two expression patterns, the first exhibited elevated total SRC coupled to low RPS6 and a significantly better clinical outcome than the second group with high RPS6 coupled to low total SRC. These results confirm the value of early detection and the potential prognostic role of the two GSC subsets, SRC and RPS6 in GMB patients.
RPPA and ONCYTE® Nitrocellulose Film Slides
The RPPA assay platform is a powerful tool for protein expression profiling, biomarker discovery, pharmacodynamics evaluation, and cancer diagnostics. Porous nitrocellulose (PNC) film slides are the premier high binding protein microarray substrate technology. The increased dynamic range for protein binding of PNC technologies with the RPPA Assay combine to create a powerful tool of patient lysate profiling.
- 3D structure offers increased surface area for binding potential
- preservation of native protein conformation
- high signal to noise ratio
- increased dynamic range for protein binding ideal for RPPA Assay
- multiplex capability
- compatible with colorimetric, chemiluminescent, fluorescent and near-IR detection strategies