Project description:Single cell RNA sequencing was performed to allow expression-based identification of tumor versus normal cells from glioblastoma patient specimens. Identified tumor cells were then analyzed to assess the expression tumor-cell specific expression of TRIM26, WWP2, and SOX2.
Project description:Glioblastoma (GBM) patient-derived orthotopic xenografts (PDOXs) were derived from organotypic spheroids obtained from patient tumor samples. To detect whether gene expression profiles of GBM patient tumors are retained in PDOXs, we performed genome-wide transcript analysis by human-specific microarrays . In parallel, we analyzed GBM cell cultures and corresponding intracranial xenografts from stem-like (NCH421k, NCH644) and adherent GBM cell lines (U87, U251). PDOXs show a better transcriptomic resemblance with patient tumors than other preclinical models. The major difference is largely explained by the depletion of human-derived non-malignant cells.
Project description:Glioblastoma is the most aggressive and lethal malignant brain tumor. miRNA expression profiling could be useful in improving the classification of tumors and predicting their behavior. In this study, the miRNA expression patterns in glioblastoma tumor tissues and adjacent normal tissues were identified through expression profiling of a patient with glioblastoma. The results will hopefully enhance our understandings of the epigentic changes in glioblastoma progression and provide candidates for miRNAs-based targeting tharapy. A paired miRNAs tumor tissues and adjacent tissues of a glioblastoma patient was used in this study. miRNAs were isolated using miRNeasy FFPE Kit (Qiagen). Profiling was established by applying the Agilent human miRNA Microarray (8 M-CM-^W 60K, v16.0) (Agilent Technologies).
Project description:Abstract. Background: Glioblastoma is the most common primary malignancy of the central nervous system with dismal prognosis. Differential gene expression classifies IDH-wildtype glioblastoma into three subtypes: proneural, mesenchymal and classical. Dasatinib, an inhibitor of the proto-oncogene tyrosine-protein kinase SRC, is one of many therapeutics that, despite promising preclinical results, has failed to improve overall survival in glioblastoma patients in clinical trials. We examined whether glioblastoma subtypes differ in their response to dasatinib therapy and could hence be used for patient enrichment strategies in clinical trials. Methods: We carried out in silico analyses of the TCGA glioblastoma gene expression data and single-cell RNA-Seq data, in combination with in vitro experiments using glioblastoma stem-like cells (GSCs) derived from primary patient tumors with complimentary gene expression profiling and immunohistochemistry analyses of tumor samples. Results: Patients suffering from the mesenchymal subtype of glioblastoma showed higher SRC pathway activation based on gene expression profiling, and accordingly, mesenchymal cells were more sensitive to SRC inhibition by dasatinib compared to proneural and classical cells. Furthermore, SERPINH1, a heat-shock protein relevant in many cancer entities, was shown to highly correlate with dasatinib response and with the mesenchymal subtype. Notably, SRC phosphorylation status was not able to reliably predict response to dasatinib treatment. Conclusion: This work highlights the need for rational methods of patient selection for clinical trials, and retrospectively sheds light on a possible gene expression subtype-led patient stratification strategy that could have improved the outcome of SRC inhibition in patients and could have implications for future trials.
Project description:Abstract. Background: Glioblastoma is the most common primary malignancy of the central nervous system with dismal prognosis. Differential gene expression classifies IDH-wildtype glioblastoma into three subtypes: proneural, mesenchymal and classical. Dasatinib, an inhibitor of the proto-oncogene tyrosine-protein kinase SRC, is one of many therapeutics that, despite promising preclinical results, has failed to improve overall survival in glioblastoma patients in clinical trials. We examined whether glioblastoma subtypes differ in their response to dasatinib therapy and could hence be used for patient enrichment strategies in clinical trials. Methods: We carried out in silico analyses of the TCGA glioblastoma gene expression data and single-cell RNA-Seq data, in combination with in vitro experiments using glioblastoma stem-like cells (GSCs) derived from primary patient tumors with complimentary gene expression profiling and immunohistochemistry analyses of tumor samples. Results: Patients suffering from the mesenchymal subtype of glioblastoma showed higher SRC pathway activation based on gene expression profiling, and accordingly, mesenchymal cells were more sensitive to SRC inhibition by dasatinib compared to proneural and classical cells. Furthermore, SERPINH1, a heat-shock protein relevant in many cancer entities, was shown to highly correlate with dasatinib response and with the mesenchymal subtype. Notably, SRC phosphorylation status was not able to reliably predict response to dasatinib treatment. Conclusion: This work highlights the need for rational methods of patient selection for clinical trials, and retrospectively sheds light on a possible gene expression subtype-led patient stratification strategy that could have improved the outcome of SRC inhibition in patients and could have implications for future trials.
Project description:Panobinostat is a non-selective histone deactylase inhibitor which has been approved by FDA for treatment of mutiple myeloma. Whether and how the drug works on glioblastoma remains unclear. Here we treated mice implanted with patient derived xenograft glioblastoma G43 with DMSO or Panobinostat and harvest the tumors for microarray analysis for gene expression results.
Project description:Glioblastoma is the most aggressive and lethal malignant brain tumor. miRNA expression profiling could be useful in improving the classification of tumors and predicting their behavior. In this study, the miRNA expression patterns in glioblastoma tumor tissues and adjacent normal tissues were identified through expression profiling of a patient with glioblastoma. The results will hopefully enhance our understandings of the epigentic changes in glioblastoma progression and provide candidates for miRNAs-based targeting tharapy.