Dataset Information

A mass spectrometry-based proteome map of drug action in lung cancer cell lines

ABSTRACT: Mass spectrometry-based discovery proteomics is an essential tool for the proximal read-out of cellular drug action. Here, we used a robust proteomic workflow to rapidly and systematically profile the proteomes of five cell lines in response to > 50 drugs. We found that aggregating millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins which frequently responded to drugs and the aggregation of proteome changes across multiple cell lines resolved compound effects on proteostasis. These characteristics were further leveraged to demonstrate efficient target identification of protein degraders. Moreover, we followed up on selected proteomic findings and showed that the inhibition of mitochondrial function is an off-target mechanism of the clinical MEK inhibitor PD184352 and that Ceritinib, an FDA approved drug in lung cancer, modulates autophagy. Overall, this study demonstrates that large-scale proteome perturbation profiling can be a useful addition to the drug discovery toolbox.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Lung

SUBMITTER: Benjamin Ruprecht

LAB HEAD: An Chi

PROVIDER: PXD018573 | Pride | 2020-08-17

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	QE-C_2018-09_A01_MTL_Calu6_25cm.raw	Raw
	QE-C_2018-09_A01_Pro10k_Calu6_P1_25cm.raw	Raw
	QE-C_2018-09_A01_Pro10k_Calu6_P2_25cm.raw	Raw
	QE-C_2018-09_A02_MTL_Calu6_25cm.raw	Raw
	QE-C_2018-09_A02_Pro10k_Calu6_P1_25cm.raw	Raw

Items per page:

1 - 5 of 215

Publications

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Ruprecht Benjamin B Di Bernardo Julie J Wang Zhao Z Mo Xuan X Ursu Oleg O Christopher Matthew M Fernandez Rafael B RB Zheng Li L Dill Brian D BD Wang Huijun H Xu Yuting Y Liaw Andy A Mortison Jonathan D JD Siriwardana Nirodhini N Andresen Brian B Glick Meir M Tata James R JR Kutilek Victoria V Cornella-Taracido Ivan I Chi An A

Nature chemical biology 20200720 10

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently respond ...[more]

PMID: 32690943

Similar Datasets

Project description:Identifying the Mechanism of Action (MoA) of drugs is critical for the development of new drugs, understanding their side effects, and drug repositioning. However, identifying drug MoA has been challenging and has been traditionally attempted only though large experimental setups with little success. While advances in computational power offers the opportunity to achieve this in-silico, methods to exploit existing computational resources are still in their infancy. To overcome this, we developed a novel method to identify Drug Mechanism of Action using Network Dysregulation (DeMAND). The method is based on the realization that drugs affect the protein activity of their targets, but not necessarily their mRNA expression levels. In contrast, the change in protein activity directly affects the mRNA expression levels of downstream genes. Based on this hypothesis, DeMAND identifies drug MoA by comparing gene expression profiles following drug perturbation with control samples, and computing the change in the individual interactions within a pre-determined integrated transcriptional and post-translational regulatory model (interactome). This dataset includes GEPs in 3 different B-cell lymphoma cell lines (OCI-LY3, OCI-LY7 and U2932) at 6, 12, and 24hrs. 92 FDA approved compounds were used at a concentration of IC20 at 24h. DMSO was used as control at each time-point. A total of 828 samples and 29 control samples were available for analysis. Total RNA was isolated with the RNAqueous-96 Automated Kit (Ambion) on the Janus automated liquid handling system (Perkin Elmer Inc.), quantified by NanoDrop 6000 spectrophotometer and quality checked by Agilent Bioanalyzer. 300ng of each of the samples with RIN value >7 were converted to biotinylated cRNA with the Illumina TotalPrep-96 RNA Amplification Kit (Ambion) using a standard T7-based amplification protocol and hybridized on the Human Genome U219 96-Array Plate (Affymetrix). Hybridization, washing, staining and scanning of the array plates were performed on the GeneTitan Instrument (Affymetrix) according to manufacturer’s protocols.