Entity

Time filter

Source Type


Chmidt L.,Sahlgrenska Cancer Center Gothenburg | Kling T.,Sahlgrenska Cancer Center Gothenburg | Monsefi N.,Sahlgrenska Cancer Center Gothenburg | Olsson M.,Sahlgrenska Cancer Center Gothenburg | And 10 more authors.
Neuro-Oncology | Year: 2013

Background. Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults, and despite stateof- the-art treatment, survival remains poor and novel therapeutics are sorely needed. The aim of the present study was to identify new synergistic drug pairs for GBM. In addition, we aimed to explore differences in drug-drug interactions across multiple GBM-derived cell cultures and predict such differences by use of transcriptional biomarkers. Methods. We performed a screen in which we quantified drug-drug interactions for 465 drug pairs in each of the 5 GBM cell lines U87MG, U343MG, U373MG, A172, and T98G. Selected interactions were further tested using isobole-based analysis and validated in 5 glioma-initiating cell cultures. Furthermore, drug interactions were predicted using microarray-based transcriptional profiling in combination with statistical modeling. Results. Of the 5 × 465 drug pairs, we could define a subset of drug pairs with strong interaction in both standard cell lines and glioma-initiating cell cultures. In particular, a subset of pairs involving the pharmaceutical compounds rimcazole, sertraline, pterostilbene, and gefitinib showed a strong interaction in a majority of the cell cultures tested. Statistical modeling of microarray and interaction data using sparse canonical correlation analysis revealed several predictive biomarkers, whichwepropose could be of importance in regulating drug pair responses. Conclusion. We identify novel candidate drug pairs for GBM and suggest possibilities to prospectively use transcriptional biomarkers to predict drug interactions in individual cases. © The Author(s) 2013. Source

Discover hidden collaborations