The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies
Year: 2020
Type: article
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.
Source: eLife
DOI: 10.7554/elife.52707
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
Awards: Storage System for High Performance Computing, A New Disease Platform Leveraging Complex Drosophila and Mammalian Models, Transforming Genomics with 5 PB Big Omics Data Engine Cray CS300-AC Supercomputer, G3549828030, Centers for Cancer Systems Therapeutics (CaST), Big Omics Data Engine 2 Supercomputer, Mount Sinai Medical Scientist Training Program, High-performance compute cluster for biomedical computing, G4806148146
OpenAlex ID: W3087098279