Abstract
Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.
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Acknowledgements
We would like to thank Tobias Herold, Wolfgang Hiddemann, Thomas Büchner, Karsten Spiekermann, Stephanie Schneider and Maria Cristina Sauerland for providing us with the patient survival information for the Herold data set. We also thank Roel Veerhak for providing us with the patient survival information for the Wouter’s data set. This work was supported by the American Cancer Society Research Grant, #IRG-82-003-30, the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001086, and by the start-up funding package provided to CC by the Geisel School of Medicine at Dartmouth College.
Author contributions
CC, C-CL (Liu) and C-CL (Lin) conceived of the project; MHU, C-HS, C-WW, C-CL (Liu) and CC performed the computational drug prediction analysis; C-CH and C-CL (Lin) performed the mouse experiments; all authors contributed to the writing of the manuscript and interpretation of results.
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Ung, M., Sun, CH., Weng, CW. et al. Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery. Pharmacogenomics J 17, 351–359 (2017). https://doi.org/10.1038/tpj.2016.18
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DOI: https://doi.org/10.1038/tpj.2016.18
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