TESTING CHEMICAL CARCINOGENICITY BY USING A TRANSCRIPTOMICS HEPARG-BASED MODEL?

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Doktorova TY
  • Yildirimman R
  • Ceelen L
  • Vilardell M
  • Vanhaecke T
  • Vinken M
  • Ates G
  • Heymans A
  • Gmuender H
  • Corvi R
  • Phrakonkham P
  • Li R
  • Mouchet N
  • Chesne C
  • van Delft J
  • Kleinjans J
  • Herwig R
  • Rogiers V

Grupos

Abstract

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances.

Datos de la publicación

ISSN/ISSNe:
1611-2156, 1611-2156

EXCLI Journal  EXCLI JOURNAL MANAGING OFFICE

Tipo:
Article
Páginas:
623-637
DOI:
PubMed:
26417288
Factor de Impacto:
0,242 SCImago
Cuartil:
Q3 SCImago

Citas Recibidas en Web of Science: 22

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Keywords

  • genotoxic carcinogens; non-genotoxic carcinogens; gene expression profiling; pathways-based analysis; HepaRG cell line; liver-based in vitro models

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