A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

Pekka Kohonen, Juuso A. Parkkinen, Egon L. Willighagen, Rebecca Ceder, Krister Wennerberg, Samuel Kaski and Roland C. Grafström ()
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Pekka Kohonen: Institute of Environmental Medicine, Karolinska Institutet
Juuso A. Parkkinen: Helsinki Institute for Information Technology HIIT, Aalto University
Egon L. Willighagen: Institute of Environmental Medicine, Karolinska Institutet
Rebecca Ceder: Institute of Environmental Medicine, Karolinska Institutet
Krister Wennerberg: Institute for Molecular Medicine Finland, FIMM, University of Helsinki
Samuel Kaski: Helsinki Institute for Information Technology HIIT, Aalto University
Roland C. Grafström: Institute of Environmental Medicine, Karolinska Institutet

Nature Communications, 2017, vol. 8, issue 1, 1-15

Abstract: Abstract Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a ‘predictive toxicogenomics space’ (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

Date: 2017
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DOI: 10.1038/ncomms15932

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