Ddis-32. Mek Inhibitors Induces Neuronal Differentiation in Egfr Amplified Glioma Stem Like Cells
Year: 2019
Type: conference-abstract
Abstract: The median survival for patients with glioblastoma (GBM) is 12–15 months highlighting the need for better therapeutic strategies for this deadly disease. Genomic and epigenomic sequencing analysis at the single cell level have identified multiple genomic aberrations as potential targets for therapeutic intervention in GBM. EGFR and PDGFR amplification are evident in nearly 40% and 12% of human GBM, respectively. Although the first and second-generation EGFR tyrosine kinase small molecule inhibitors failed to show long term therapeutic benefit in GBM patients, multiple factors such as incorrect patient selection, acquired resistance, and drug-target heterogeneity may all lead to clinical failure of targeted therapies. Although the multilevel genomic characterization of gliomas are increasing, the clinical translation of these findings is beginning to unravel. In this study, we attempted to correlate the genomic variations using an unbiased high throughput drug screen using primary glioma stem-like cell (GSCs) as our model system. An unbiased high-throughput screen utilizing our GSC models identified that glioblastoma cells harboring focal EGFR amplification are sensitive to mitogen-activated protein kinase (MEK) inhibitors. MEK inhibition induced apoptosis in EGFR amplified cells at low concentration. RNA sequence analysis of cells treated with MEK inhibitors revealed upregulation of genes related to neuronal differentiation and down regulation of MEK target genes in MEK sensitive glioma stem cells. Additionally, RNA sequencing of GSCs with acquired MEK inhibitor resistance demonstrated an upregulation of oncogenic transcription factor ETS Variant Gene 1 (ETV1) as a mediator of resistance. Overall our data suggest that the MEK inhibition in combination with ETV inhibitors could be a potential therapeutic target for a subset of GBM patients.
Source: Neuro-Oncology
Authors: Veerakumar Balasubramaniyan, Soon Park, Yuji Piao, Emmanuel Martínez, Jianwen Dong, Sandeep Mittal, Sabbir Khan, Ze-yan Zhang, Erik Sulman, John DeGroot
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2983478313