Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Rattus norvegicus
PROVIDER: GSE53085 | GEO | 2014/08/18
SECONDARY ACCESSION(S): PRJNA230813
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-08-18 | E-GEOD-53085 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53084 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53083 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53082 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53083 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53082 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53084 | biostudies-arrayexpress
Project description:Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat
| PRJNA230813 | ENA
Project description: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.
| S-EPMC4022579 | biostudies-literature
Project description:The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3, Cdkn1a, Akr7a3, Ccng1 and Abcb4.
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