Signatures of cell death and proliferation in perturbation transcriptomics data - from confounding factor to effective prediction

Abstract

Transcriptomics perturbation signatures are valuable data sources for functional genomic studies. They can be effectively used to identify mechanism of action for new compounds and to infer functional activity of different cellular processes. Linking perturbation signatures to phenotypic studies opens up the possibility to model selected cellular phenotypes from gene expression data and to predict drugs interfering with the phenotype. At the same time, close association of transcriptomics changes with phenotypes can potentially mask the compound specific signatures. By linking perturbation transcriptomics data from the LINCS-L1000 project with cell viability phenotypic information upon genetic (from Achilles project) and chemical (from CTRP screen) perturbations for more than 90,000 signature - cell viability pairs, we show here that a cell death signature is a major factor behind perturbation signatures. We use this relationship to effectively predict cell viability from transcriptomics signatures, and identify and experimentally validate compounds that induce either cell death or proliferation. We also show that cellular toxicity can lead to an unexpected similarity of toxic compound signatures confounding the mechanism of action discovery. Consensus compound signatures predict cell-specific anti-cancer drug sensitivity, even if the drug signature is not measured in the same cell line. These signatures outperform conventional drug-specific features like nominal target and chemical fingerprints. Our results can help removing confounding factors of large scale transcriptomics perturbation screens and show that expression signatures boost prediction of drug sensitivity.