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Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

Author

Listed:
  • Mario Niepel

    (Harvard Medical School)

  • Marc Hafner

    (Harvard Medical School)

  • Qiaonan Duan

    (BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai)

  • Zichen Wang

    (BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai)

  • Evan O. Paull

    (University of California)

  • Mirra Chung

    (Harvard Medical School)

  • Xiaodong Lu

    (Broad Institute of MIT and Harvard University)

  • Joshua M. Stuart

    (University of California)

  • Todd R. Golub

    (Broad Institute of MIT and Harvard University
    Dana-Farber Cancer Institute
    Howard Hughes Medical Institute)

  • Aravind Subramanian

    (Broad Institute of MIT and Harvard University)

  • Avi Ma’ayan

    (BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai)

  • Peter K. Sorger

    (Harvard Medical School)

Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Suggested Citation

  • Mario Niepel & Marc Hafner & Qiaonan Duan & Zichen Wang & Evan O. Paull & Mirra Chung & Xiaodong Lu & Joshua M. Stuart & Todd R. Golub & Aravind Subramanian & Avi Ma’ayan & Peter K. Sorger, 2017. "Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01383-w
    DOI: 10.1038/s41467-017-01383-w
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