bioRxivCancer Biology
Mutational landscape of a chemically-induced mouse model of liver cancer
F. Connor, T. F. Rayner, C. Feig, S. J. Aitken, M. Lukk, D. T. Odom
Abstract
Carcinogen-induced mouse models of liver cancer are used extensively to study the pathogenesis of the disease and have a critical role in validating candidate therapeutics. These models can recapitulate molecular and histological features of human disease. However, it is not known if the spectra of genomic alterations driving these mouse tumour genomes are comparable to those found in humans. Here, we provide a detailed characterisation of the exome-wide pattern of mutations in tumours from mice exposed to diethylnitrosamine (DEN), a commonly used model of hepatocellular carcinoma (HCC). DEN-initiated tumours had a high, uniform number of somatic single nucleotide variants (SNVs), with very few insertions, deletions or copy number alterations, consistent with the known genotoxic action of DEN. Exposure of hepatocytes to DEN left a reproducible mutational imprint in resulting tumour exomes which we could computationally reconstruct using six known COSMIC mutational signatures. The tumours carried a high diversity of low-incidence, non-synonymous point mutations in many oncogenes and tumour suppressors, reflecting the stochastic introduction of SNVs into the hepatocyte genome by the carcinogen. We identified four recurrently mutated genes that were putative oncogenic drivers of HCC in this model. Every neoplasm carried activating hotspot mutations either in codon 61 of Hras, in codon 584 of Braf or in codon 254 of Egfr. Truncating mutations of Apc occurred in 21% of neoplasms, which were exclusively carcinomas supporting a role for deregulation of Wnt/{beta}-catenin signalling in cancer progression. Conclusion: Our study provides detailed insight into the mutational landscape of tumours arising in a commonly-used carcinogen model of hepatocellular carcinoma, facilitating the future use of this model to understand the human disease.
- organism: mouse
- assay: whole-exome sequencing
- research focus: variant calling
- approach: statistical model
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