Dataset Information

Prediction of interindividual differences in hepatic functions and drug sensitivity by using human iPS-derived hepatocytes.

ABSTRACT: Interindividual differences in hepatic metabolism, which are mainly due to genetic polymorphism in its gene, have a large influence on individual drug efficacy and adverse reaction. Hepatocyte-like cells (HLCs) differentiated from human induced pluripotent stem (iPS) cells have the potential to predict interindividual differences in drug metabolism capacity and drug response. However, it remains uncertain whether human iPSC-derived HLCs can reproduce the interindividual difference in hepatic metabolism and drug response. We found that cytochrome P450 (CYP) metabolism capacity and drug responsiveness of the primary human hepatocytes (PHH)-iPS-HLCs were highly correlated with those of PHHs, suggesting that the PHH-iPS-HLCs retained donor-specific CYP metabolism capacity and drug responsiveness. We also demonstrated that the interindividual differences, which are due to the diversity of individual SNPs in the CYP gene, could also be reproduced in PHH-iPS-HLCs. We succeeded in establishing, to our knowledge, the first PHH-iPS-HLC panel that reflects the interindividual differences of hepatic drug-metabolizing capacity and drug responsiveness.

SUBMITTER: Takayama K

PROVIDER: S-EPMC4250156 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Prediction of interindividual differences in hepatic functions and drug sensitivity by using human iPS-derived hepatocytes.

Takayama Kazuo K Morisaki Yuta Y Kuno Shuichi S Nagamoto Yasuhito Y Harada Kazuo K Furukawa Norihisa N Ohtaka Manami M Nishimura Ken K Imagawa Kazuo K Sakurai Fuminori F Tachibana Masashi M Sumazaki Ryo R Noguchi Emiko E Nakanishi Mahito M Hirata Kazumasa K Kawabata Kenji K Mizuguchi Hiroyuki H

Proceedings of the National Academy of Sciences of the United States of America 20141110 47

Interindividual differences in hepatic metabolism, which are mainly due to genetic polymorphism in its gene, have a large influence on individual drug efficacy and adverse reaction. Hepatocyte-like cells (HLCs) differentiated from human induced pluripotent stem (iPS) cells have the potential to predict interindividual differences in drug metabolism capacity and drug response. However, it remains uncertain whether human iPSC-derived HLCs can reproduce the interindividual difference in hepatic met ...[more]

PMID: 25385620

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Project description:BackgroundDrug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study.MethodsIn this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction .ResultsIn the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug's mechanism of action.ConclusionsNetwork-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets.

Dataset Information

Prediction of interindividual differences in hepatic functions and drug sensitivity by using human iPS-derived hepatocytes.

Publications

Prediction of interindividual differences in hepatic functions and drug sensitivity by using human iPS-derived hepatocytes.

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