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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 und Spang, Rainer (2014) Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro. PLoS ONE 9 (9), e108632.Veröffentlichungsdatum dieses Volltextes: 06 Okt 2014 13:16
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DOI zum Zitieren dieses Dokuments: 10.5283/epub.30851
Zusammenfassung
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.
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| Dokumentenart | Artikel | ||||
| Titel eines Journals oder einer Zeitschrift | PLoS ONE | ||||
| Verlag: | PUBLIC LIBRARY SCIENCE | ||||
|---|---|---|---|---|---|
| Ort der Veröffentlichung: | SAN FRANCISCO | ||||
| Band: | 9 | ||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 9 | ||||
| Seitenbereich: | e108632 | ||||
| Datum | 30 September 2014 | ||||
| Institutionen | Medizin > Lehrstuhl für Neurochirurgie Medizin > Lehrstuhl für Neurologie Medizin > Lehrstuhl für Pathologie Medizin > Zentren des Universitätsklinikums Regensburg > Zentrum für Hirntumore (ZHT) Medizin > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Informatik und Data Science > Fachbereich Bioinformatik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) | ||||
| Identifikationsnummer |
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| Stichwörter / Keywords | ENDOTHELIAL GROWTH-FACTOR; STEM-CELLS; TUMOR-CELLS; SUNITINIB; CANCER; RECEPTORS; MIGRATION; IDENTIFICATION; RESISTANCE; PATTERN; | ||||
| Dewey-Dezimal-Klassifikation | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin | ||||
| Status | Veröffentlicht | ||||
| Begutachtet | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden | Ja | ||||
| URN der UB Regensburg | urn:nbn:de:bvb:355-epub-308511 | ||||
| Dokumenten-ID | 30851 |
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