@article{516e7f1e732f4fd3aeb735c2938ea870,
title = "The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies",
abstract = "Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.",
author = "Diaz, {Jennifer E.L.} and Ahsen, {Mehmet Eren} and Thomas Schaffter and Xintong Chen and Realubit, {Ronald B.} and Charles Karan and Andrea Califano and Bojan Losic and Gustavo Stolovitzky",
note = "Funding Information: We thank Mukesh Bansal, James Bieker, Ross Cagan, Jing He, and Carlos Villacorta for useful discussions. We thank the authors of Sirci et al. for sharing the PLD gene signature. We also acknowledge support to Jennifer Diaz from NIH grants T32 GM007280 and NIH-U54OD020353, and to Andrea Califano from U54 CA209997 (Cancer Systems Biology Consortium), S10 OD012351 and S10 OD021764 (Shared Instrument Grants). Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD018522 and S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2020, eLife Sciences Publications Ltd. All rights reserved.",
year = "2020",
month = sep,
doi = "10.7554/ELIFE.52707",
language = "English",
volume = "9",
pages = "1--62",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",
}