The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Abstract
Data availability
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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.

Data availability

Raw RNAseq data have been deposited in GEO under accession code GSE149428. Code is available at github.com/jennifereldiaz/drug-synergy.

The following previously published data sets were used

1. Bansal M
2. Yang J
3. Karan C
4. Menden MP
5. Costello JC
6. Tang H
7. Xiao G Li Y
8. Allen J
9. Zhong R
10. Chen B
11. Kim M
12. Wang T
13. Heiser LM
14. Realubit R
15. Mattioli M
16. Alvarez MJ
17. Shen Y
18. NCI-DREAM Community
19. Gallahan D
20. Singer D
21. Saez-Rodriguez J
22. Xie Y
23. Stolovitzky G
24. Califano A
25. NCI-DREAM Community
(2014) sub challenge 2, Drug Synergy Prediction
Synapse, syn2785787.

https://www.synapse.org/#!Synapse:syn2785787

Article and author information

Author details

Jennifer EL Diaz

Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

Competing interests
No competing interests declared.
Mehmet Eren Ahsen

Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

Competing interests
No competing interests declared.
Thomas Schaffter

IBM Computational Biology Center, IBM Research, Yorktown Heights, United States

Competing interests
No competing interests declared.
Xintong Chen

Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

Competing interests
No competing interests declared.
Ronald B Realubit

Department of Systems Biology, Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, United States

Competing interests
No competing interests declared.
Charles Karan

Department of Systems Biology, Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, United States

Competing interests
No competing interests declared.
Andrea Califano

Department of Systems Biology, Department of Biomedical Informatics, Department of Biochemistry and Molecular Biophysics, J.P. Sulzberger Columbia Genome Center, Herbert Irving Comprehensive Cancer, Columbia University, New York, United States

Competing interests
Andrea Califano, Dr. Califano is founder, equity holder, consultant, and director of DarwinHealth Inc., a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth Inc..
Bojan Losic

Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, United States

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-3643-4612
Gustavo Stolovitzky

IBM Computational Biology Center, IBM Research, Yorktown Heights, United States

For correspondence
gustavo@us.ibm.com

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0002-9618-2819

Funding

Internal funding from IBM Research and the Icahn School of Medicine at Mount Sinai to GS

Jennifer EL Diaz
Mehmet Eren Ahsen
Thomas Schaffter
Xintong Chen
Bojan Losic
Gustavo Stolovitzky

National Institutes of Health (NIH T32 GM007280)

Jennifer EL Diaz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.