Dr.Paso: Drug response prediction and analysis system for oncology research

Azuaje, F.; Kaoma, T.; Jeanty, C.; Nazarov, P. V.; Muller, A.; Kim, S.-Y.; Golebiewska, A.; Dittmar, G.; Niclou, S. P.

doi:10.1101/237727

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bioRxiv · 10.1101/237727

Dr.Paso: Drug response prediction and analysis system for oncology research

Azuaje, F.·Kaoma, T.·Jeanty, C.·Nazarov, P. V.·Muller, A.·Kim, S.-Y.·Golebiewska, A.·Dittmar, G.·Niclou, S. P.

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Abstract

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

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Azuaje, F., Kaoma, T., Jeanty, C., Nazarov, P. V., Muller, A., Kim, S.-Y., Golebiewska, A., Dittmar, G., Niclou, S. P.. 2017-12-21. Dr.Paso: Drug response prediction and analysis system for oncology research. https://doi.org/10.1101/237727

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