Microarray Analysis in Glioblastomas.

Microarray analysis in glioblastomas is done using either cell lines or patient samples as starting material. A survey of the current literature points to transcript-based microarrays and immunohistochemistry (IHC)-based tissue microarrays as being the preferred methods of choice in cancers of neurological origin. Microarray analysis may be carried out for various purposes including the following: i. To correlate gene expression signatures of glioblastoma cell lines or tumors with response to chemotherapy (DeLay et al., Clin Cancer Res 18(10):2930-2942, 2012). ii. To correlate gene expression patterns with biological features like proliferation or invasiveness of the glioblastoma cells (Jiang et al., PLoS One 8(6):e66008, 2013). iii. To discover new tumor classificatory systems based on gene expression signature, and to correlate therapeutic response and prognosis with these signatures (Huse et al., Annu Rev Med 64(1):59-70, 2013; Verhaak et al., Cancer Cell 17(1):98-110, 2010). While investigators can sometimes use archived tumor gene expression data available from repositories such as the NCBI Gene Expression Omnibus to answer their questions, new arrays must often be run to adequately answer specific questions. Here, we provide a detailed description of microarray methodologies, how to select the appropriate methodology for a given question, and analytical strategies that can be used. Experimental methodology for protein microarrays is outside the scope of this chapter, but basic sample preparation techniques for transcript-based microarrays are included here.

An automated image capture and quantitation approach to identify proteins affecting tumor cell proliferation.

To facilitate the characterization of proteins that negatively regulate tumor cell proliferation in vitro, the authors have implemented a high-throughput functional assay that measures S-phase progression of tumor cell lines. For 2 tumor cell lines-human melanoma A375 and human lung carcinoma A549-conditions were established using the cyclin-dependent kinase inhibitor, p27kip; the tumor suppressor p53, a kinase-inactive allele of the cell cycle-regulated serine/threonine kinase Aurora2; and the G1/S drug block, aphidicolin. For screening purposes, gene libraries were delivered by adenoviral infection. Cells were fixed and labeled by immunocytochemistry, and an automated image acquisition and analysis package on a Cellomics ArrayScanII was used to quantify the effects of these treatments on cell proliferation. The assay can be used to identify novel proteins involved in proliferation and serves as a more robust, reproducible, and sensitive alternative to enzyme-linked immunosorbent assay (ELISA)-based technologies.