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

Abstract

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL).

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

“…Other groups are generating proteomic or high-content imaging readouts following perturbation (Litichevskiy et al, 2017); (Rohban et al, 2017) consistent with early reports of feasibility of annotating compounds based on cellular consequences (Seiler et al, 2008). …”

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

“…Because of the lack of adjacent normal tissue of individuals with HGG, we chose to derive RNA/proteomics signatures of HGG aggressiveness by comparing HGG and LGG tumors. We then leveraged the LINCS L1000 transcriptomics and P100 phosphoproteomics perturbation-response databases to search for candidate drugs inducing effects that oppose the corresponding input (Litichevskiy et al, 2018;Subramanian et al, 2017; STAR Methods; Table S6). CDK inhibitors were predicted to reverse the aggressiveness of HGG based on RNA and phosphoproteomics data (Figures 6G, S6F, and S6G; Table S6).…”

“…For the phosphoproteomic connectivity analysis, protein and phosphopeptide signatures were calculated by comparing HGG and LGG samples via the Wilcoxon rank sum test. Significant phosphopeptide and protein probes (FDR < 0.05) were then mapped to the P100 peptide probes (Litichevskiy et al, 2018) and were used for subsequent analysis. Level 4 P100 data were downloaded from the LINCS Data Portal (Stathias et al, 2020) and the median of each technical replicate was used to calculate the spearman correlation between each P100 experiment and the HGG-specific phosphoproteomic signature.…”

Targeted Proteomics Comes to the Benchside and the Bedside: Is it Ready for Us?

“…When this method becomes mainstream, a vast amount of stored FFPE samples in clinics can be used . Similarly, quantitative measurement of drug‐metabolizing enzymes and transporters using targeted proteomics can provide insights and monitoring of the longitudinal therapeutic response of patients …”