Abstract
Background
Glioblastoma multiforme (GBM) is the most aggressive brain tumor in
adults, and despite state-of-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,
which
we propose 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.
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