| Download ( PDF | 1MB) |
Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro
Hau, Peter, Moeckel, Sylvia, Meyer, Katharina, Leukel, Petra, Heudorfer, Fabian, Seliger, Corinna, Stangl, Christina, Bogdahn, Ulrich, Proescholdt, Martin A., Brawanski, Alexander, Vollmann-Zwerenz, Arabel, Riemenschneider, Markus J., Bosserhoff, Anja-Katrin and Spang, Rainer (2014) Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro. PLoS ONE 9 (9), e108632.Date of publication of this fulltext: 06 Oct 2014 13:16
Article
DOI to cite this document: 10.5283/epub.30851
Abstract
Background: High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist. Methods: In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response ...
Background: High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist. Methods: In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a) to enrich specimens for brain tumor initiating cells and (b) to confront cells with a therapeutic agent before expression profiling. Results: As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro. Conclusion: For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.
Alternative links to fulltext
Involved Institutions
Details
| Item type | Article | ||||
| Journal or Publication Title | PLoS ONE | ||||
| Publisher: | PUBLIC LIBRARY SCIENCE | ||||
|---|---|---|---|---|---|
| Place of Publication: | SAN FRANCISCO | ||||
| Volume: | 9 | ||||
| Number of Issue or Book Chapter: | 9 | ||||
| Page Range: | e108632 | ||||
| Date | 30 September 2014 | ||||
| Institutions | Medicine > Lehrstuhl für Neurochirurgie Medicine > Lehrstuhl für Neurologie Medicine > Lehrstuhl für Pathologie Medicine > Zentren des Universitätsklinikums Regensburg > Zentrum für Hirntumore (ZHT) Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Informatics and Data Science > Department Computational Life Science > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) | ||||
| Identification Number |
| ||||
| Keywords | ENDOTHELIAL GROWTH-FACTOR; STEM-CELLS; TUMOR-CELLS; SUNITINIB; CANCER; RECEPTORS; MIGRATION; IDENTIFICATION; RESISTANCE; PATTERN; | ||||
| Dewey Decimal Classification | 600 Technology > 610 Medical sciences Medicine | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Yes | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-308511 | ||||
| Item ID | 30851 |
Download Statistics
Download Statistics