Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.
Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.
Michael
M
Römer
Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.
Johannes
J
Eichner
Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
Ute
U
Metzger
Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
Markus F
MF
Templin
CXR Biosciences, James Lindsay Place, Dundee Technopole, Dundee, Scotland, United Kingdom.
Simon
S
Plummer
Bayer Pharma AG, Wuppertal, Germany.
Heidrun
H
Ellinger-Ziegelbauer
Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.
Andreas
A
Zell
D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D001185:Artificial Intelligence; D015197:Carcinogenicity Tests; D002273:Carcinogens / Q000737:chemistry*; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D008114:Liver Neoplasms, Experimental / Q000139:chemically induced / Q000378:metabolism*; D008297:Male; D015233:Models, Statistical; D040081:Protein Array Analysis; D012333:RNA, Messenger / Q000378:metabolism; D051381:Rats; D017208:Rats, Wistar; D043922:Toxicogenetics / Q000379:methods*
D002273:Carcinogens; D012333:RNA, Messenger