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

Author:

Diaz Jennifer EL123ORCID,Ahsen Mehmet Eren134ORCID,Schaffter Thomas13,Chen Xintong1,Realubit Ronald B56,Karan Charles56,Califano Andrea5789,Losic Bojan1101112ORCID,Stolovitzky Gustavo1357ORCID

Affiliation:

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

2. Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, United States

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

4. Department of Business Administration, University of Illinois at Urbana-Champaign, Champaign, United States

5. Department of Systems Biology, Columbia University, New York, United States

6. Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia University Medical Center, New York, United States

7. Department of Biomedical Informatics, Columbia University, New York, United States

8. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

9. Department of Medicine, Columbia University, New York, United States

10. Tisch Cancer Institute, Cancer Immunology, Icahn School of Medicine at Mount Sinai, New York, United States

11. Diabetes, Obesity and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, United States

12. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, United States

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.

Funder

IBM

National Institutes of Health

National Cancer Institute

Icahn School of Medicine at Mount Sinai

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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