Diversity in responses to oncolytic Lassa-vesicular stomatitis virus in patient-derived glioblastoma cells

The difficulty of glioblastoma treatment makes it a good candidate for novel therapies, such as oncolytic viruses. Vesicular stomatitis virus expressing Lassa virus glycoprotein (Lassa-VSV) showed significant promise in animal models using established glioblastoma cell lines. These experiments were to determine the susceptibility of low-passage, patient-derived cell lines to Lassa-VSV oncolysis. Four patient-derived glioblastoma cell lines were infected with Lassa-VSV that expresses green fluorescent protein (GFP) and analyzed by fluorescence microscopy, flow cytometry, and cell viability assays. Cells were also analyzed as tumorspheres containing primarily glioma stem-like cells. Three low-passage, patient-derived cells were further analyzed with RNA sequencing (RNA-seq). Individual cell lines varied somewhat in their levels of viral gene expression and time course of Lassa-VSV-induced cell death, but each was susceptible to Lassa-VSV. Brain Tumor Center of Excellence (BTCOE) 4765 cells had the highest level of expression of interferon-stimulated genes but were most susceptible to Lassa-VSV-induced cell death, indicating that more susceptible cells do not necessarily have lower interferon pathway activation. Cells cultured as tumorspheres and infected with Lassa-VSV also showed variable susceptibility to Lassa-VSV, but BTCOE 4765 cells were least susceptible. Thus, patient-derived brain tumor cells show variable responses to Lassa-VSV infection, but each of the lines was susceptible to VSV oncolysis.

Targeting the mesenchymal subtype in glioblastoma and other cancers via inhibition of diacylglycerol kinase alpha

Background

The mesenchymal phenotype in glioblastoma (GBM) and other cancers drives aggressiveness and treatment resistance, leading to therapeutic failure and recurrence of disease. Currently, there is no successful treatment option available against the mesenchymal phenotype.

Methods

We classified patient-derived GBM stem cell lines into 3 subtypes: proneural, mesenchymal, and other/classical. Each subtype's response to the inhibition of diacylglycerol kinase alpha (DGKα) was compared both in vitro and in vivo. RhoA activation, liposome binding, immunoblot, and kinase assays were utilized to elucidate the novel link between DGKα and geranylgeranyltransferase I (GGTase I).

Results

Here we show that inhibition of DGKα with a small-molecule inhibitor, ritanserin, or RNA interference preferentially targets the mesenchymal subtype of GBM. We show that the mesenchymal phenotype creates the sensitivity to DGKα inhibition; shifting GBM cells from the proneural to the mesenchymal subtype increases ritanserin activity, with similar effects in epithelial-mesenchymal transition models of lung and pancreatic carcinoma. This enhanced sensitivity of mesenchymal cancer cells to ritanserin is through inhibition of GGTase I and downstream mediators previously associated with the mesenchymal cancer phenotype, including RhoA and nuclear factor-kappaB. DGKα inhibition is synergistic with both radiation and imatinib, a drug preferentially affecting proneural GBM.

Conclusions

Our findings demonstrate that a DGKα-GGTase I pathway can be targeted to combat the treatment-resistant mesenchymal cancer phenotype. Combining therapies with greater activity against each GBM subtype may represent a viable therapeutic option against GBM.

Master regulators, regulatory networks, and pathways of glioblastoma subtypes

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype- specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.