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Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro

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 thi...

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Detalles Bibliográficos
Autores principales: Moeckel, Sylvia, Meyer, Katharina, Leukel, Petra, Heudorfer, Fabian, Seliger, Corinna, Stangl, Christina, Bogdahn, Ulrich, Proescholdt, Martin, Brawanski, Alexander, Vollmann-Zwerenz, Arabel, Riemenschneider, Markus J., Bosserhoff, Anja-Katrin, Spang, Rainer, Hau, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182559/
https://www.ncbi.nlm.nih.gov/pubmed/25268354
http://dx.doi.org/10.1371/journal.pone.0108632
Descripción
Sumario: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.