1. Cell Biology
  2. Computational and Systems Biology

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

  1. Jennifer EL Diaz
  2. Mehmet Eren Ahsen
  3. Thomas Schaffter
  4. Xintong Chen
  5. Ronald B Realubit
  6. Charles Karan
  7. Andrea Califano
  8. Bojan Losic
  9. Gustavo Stolovitzky  Is a corresponding author
  1. Icahn School of Medicine at Mount Sinai, United States
  2. IBM Research, United States
  3. Columbia University, United States
https://doi.org/10.7554/eLife.52707
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Cite this article
  1. Jennifer EL Diaz
  2. Mehmet Eren Ahsen
  3. Thomas Schaffter
  4. Xintong Chen
  5. Ronald B Realubit
  6. Charles Karan
  7. Andrea Califano
  8. Bojan Losic
  9. Gustavo Stolovitzky
(2020)
The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies
eLife 9:e52707.
https://doi.org/10.7554/eLife.52707
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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

Article and author information

Author details

  1. 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.

  2. 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.

  3. Thomas Schaffter

    IBM Computational Biology Center, IBM Research, Yorktown Heights, United States
    Competing interests
    No competing interests declared.

  4. 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.

  5. 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.

  6. 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.

  7. 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..

  8. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3643-4612

  9. Gustavo Stolovitzky

    IBM Computational Biology Center, IBM Research, Yorktown Heights, United States
    For correspondence
    gustavo@us.ibm.com
    Competing interests
    No competing interests declared.
    ORCID icon "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.

Reviewing Editor

  1. Alfonso Valencia, Barcelona Supercomputing Center - BSC, Spain

Version history

  1. Received: October 13, 2019
  2. Accepted: August 17, 2020
  3. Accepted Manuscript published: September 18, 2020 (version 1)
  4. Version of Record published: October 9, 2020 (version 2)

Copyright

© 2020, Diaz et al.

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.

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  1. Jennifer EL Diaz
  2. Mehmet Eren Ahsen
  3. Thomas Schaffter
  4. Xintong Chen
  5. Ronald B Realubit
  6. Charles Karan
  7. Andrea Califano
  8. Bojan Losic
  9. Gustavo Stolovitzky
(2020)
The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies
eLife 9:e52707.
https://doi.org/10.7554/eLife.52707

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