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Modelling mutational landscapes of human cancers in vitro

Experimental models that recapitulate mutational landscapes of human cancers are needed to decipher the rapidly expanding data on human somatic mutations. We demonstrate that mutation patterns in immortalised cell lines derived from primary murine embryonic fibroblasts (MEFs) exposed in vitro to car...

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Detalles Bibliográficos
Autores principales: Olivier, Magali, Weninger, Annette, Ardin, Maude, Huskova, Hana, Castells, Xavier, Vallée, Maxime P., McKay, James, Nedelko, Tatiana, Muehlbauer, Karl-Rudolf, Marusawa, Hiroyuki, Alexander, John, Hazelwood, Lee, Byrnes, Graham, Hollstein, Monica, Zavadil, Jiri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5259794/
https://www.ncbi.nlm.nih.gov/pubmed/24670820
http://dx.doi.org/10.1038/srep04482
Descripción
Sumario:Experimental models that recapitulate mutational landscapes of human cancers are needed to decipher the rapidly expanding data on human somatic mutations. We demonstrate that mutation patterns in immortalised cell lines derived from primary murine embryonic fibroblasts (MEFs) exposed in vitro to carcinogens recapitulate key features of mutational signatures observed in human cancers. In experiments with several cancer-causing agents we obtained high genome-wide concordance between human tumour mutation data and in vitro data with respect to predominant substitution types, strand bias and sequence context. Moreover, we found signature mutations in well-studied human cancer driver genes. To explore endogenous mutagenesis, we used MEFs ectopically expressing activation-induced cytidine deaminase (AID) and observed an excess of AID signature mutations in immortalised cell lines compared to their non-transgenic counterparts. MEF immortalisation is thus a simple and powerful strategy for modelling cancer mutation landscapes that facilitates the interpretation of human tumour genome-wide sequencing data.