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Genomic indicators in the blood predict drug-induced liver injury

The Pharmacogenomics Journal volume 10, pages 267–277 (2010)Cite this article

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Abstract

Genomic biomarkers for the detection of drug-induced liver injury (DILI) from blood are urgently needed for monitoring drug safety. We used a unique data set as part of the Food and Drug Administration led MicroArray Quality Control Phase-II (MAQC-II) project consisting of gene expression data from the two tissues (blood and liver) to test cross-tissue predictability of genomic indicators to a form of chemically induced liver injury. We then use the genomic indicators from the blood as biomarkers for prediction of acetaminophen-induced liver injury and show that the cross-tissue predictability of a response to the pharmaceutical agent (accuracy as high as 92.1%) is better than, or at least comparable to, that of non-therapeutic compounds. We provide a database of gene expression for the highly informative predictors, which brings biological context to the possible mechanisms involved in DILI. Pathway-based predictors were associated with inflammation, angiogenesis, Toll-like receptor signaling, apoptosis, and mitochondrial damage. The results show for the first time and support the hypothesis that genomic indicators in the blood can serve as potential diagnostic biomarkers predictive of DILI.

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Acknowledgements

We thank the National Center for Toxicogenomics at the National Institute of Environmental Health Sciences (NIEHS) for the hepatotoxicant compendium data. In addition, we thank the participants of MAQC-II for comments, feedback, and discussions on the topic of this paper during teleconferences and face-to-face project meetings. We also thank K Shockley, A Merrick, S Hester, B Ward, and D Mendrick for their critical review of the manuscript. JH acknowledge the support of the Oak Ridge Institute for Science and Education (ORISE) for the Post-graduate Research Program at the National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA). JH also acknowledges the support of the China State-funded Study Abroad Program that is organized by the China Scholarship Council (CSC). JH and XF both acknowledge the Chinese Key Technologies R&D Program (No. 2005CB23402) and the National Science Foundation of China (No. 30801556) for support to participate in the MAQC-II project at the NCTR/FDA. This research was supported, in part by, the Intramural Research Program of the NIH and NIEHS (Z01 ES102345-03). This document has been reviewed in accordance with the US FDA and Environmental Protection Agency (EPA) policies and is approved for publication. Approval does not signify that the contents necessarily reflect the position or opinions of the FDA or EPA nor does mention of trade names or commercial products constitute endorsement or recommendation for use. The findings, views, and conclusions in this report are those of the authors and do not necessarily represent or reflect the views of the FDA or EPA.

Author information

Author notes

  1. J W Chou

    Present address: 13Current address: Department of Biostatistical Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA.,

  2. J Li

    Present address: 14Current address: Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, 130 Mason Farm Road, Chapel Hill, NC 27599, USA.,

  3. J Huang, W Shi and J Zhang: These authors contributed equally to this work.

Authors and Affiliations

  1. Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China

    J Huang, X Fan & Y Cheng

  2. GeneGO, St Joseph, MI, USA

    W Shi, Y Nikolsky & T Nikolskaya

  3. Department of Bioinformatics, Systems Analytics, Waltham, MA, USA

    J Zhang & J Luo

  4. Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA

    J W Chou, J Li & P R Bushel

  5. Microarray Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA

    R S Paules & K Gerrish

  6. Genomics Division, SAS, Cary, NC, USA

    R D Wolfinger, W Bao & T-M Chu

  7. Vavilov Institute for General Genetics, Russian Academy of Sciences, Moscow, Russia

    D Dosymbekov & M O Tsyganova

  8. Center for Toxicoinformatics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA

    J Huang, L Shi, X Fan, M Chen & W Tong

  9. National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA

    J C Corton

  10. Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/Food and Drug Administration, Jefferson, AR, USA

    H Fang

  11. Systems Biology Laboratory, Institute for General Genetics, Moscow, Russia

    T Nikolskaya

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  1. J Huang
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Correspondence to P R Bushel.

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Huang, J., Shi, W., Zhang, J. et al. Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J 10, 267–277 (2010). https://doi.org/10.1038/tpj.2010.33

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