A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

  • Cell Syst. 2018 Apr 25;6(4):424-443.e7. doi: 10.1016/j.cels.2018.03.012.
Lev Litichevskiy  1 ,  Ryan Peckner  1 ,  Jennifer G Abelin  1 ,  Jacob K Asiedu  1 ,  Amanda L Creech  1 ,  John F Davis  1 ,  Desiree Davison  1 ,  Caitlin M Dunning  1 ,  Jarrett D Egertson  2 ,  Shawn Egri  1 ,  Joshua Gould  1 ,  Tak Ko  3 ,  Sarah A Johnson  1 ,  David L Lahr  1 ,  Daniel Lam  1 ,  Zihan Liu  1 ,  Nicholas J Lyons  1 ,  Xiaodong Lu  1 ,  Brendan X MacLean  2 ,  Alison E Mungenast  3 ,  Adam Officer  1 ,  Ted E Natoli  1 ,  Malvina Papanastasiou  1 ,  Jinal Patel  1 ,  Vagisha Sharma  2 ,  Courtney Toder  1 ,  Andrew A Tubelli  1 ,  Jennie Z Young  3 ,  Steven A Carr  1 ,  Todd R Golub  1 ,  Aravind Subramanian  1 ,  Michael J MacCoss  2 ,  Li-Huei Tsai  3 ,  Jacob D Jaffe  4
Affiliations
  • 1. The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA.
  • 2. University of Washington, Department of Genome Sciences, 3720 15th Avenue NE, Seattle, WA 98195, USA.
  • 3. Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
  • 4. The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA. Electronic address: [email protected].
PMID: 29655704 DOI: 10.1016/j.cels.2018.03.012
Abstract

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

Keywords
GCP; L1000; LINCS project; P100; drug discovery; epigenetics; mass spectrometry; mechanism of action; proteomics; signaling.
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