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

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 and Peter K. Sorger ()
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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
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

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-01383-w

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