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

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. A large compendium of cellular responses to drugs as profiled through proteomic assays of phosphosignaling and histone modifications reveals cellular responses that transcend lineage, discovers unexpected associations between drugs, and recognizes therapeutic hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. Keywords: mass spectrometry; proteomics; drug discovery; signaling; epigenetics; mechanism of action; LINCS project; GCP; P100; L1000NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008097)NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008699

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

ISSN:1548-7105ISSN:1548-709