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The mutational signature profile of known and suspected human carcinogens in mice


Epidemiological studies have identified many environmental agents that appear to significantly increase cancer risk in human populations. By analyzing tumor genomes from mice chronically exposed to 1 of 20 known or suspected human carcinogens, we reveal that most agents do not generate distinct mutational signatures or increase mutation burden, with most mutations, including driver mutations, resulting from tissue-specific endogenous processes. We identify signatures resulting from exposure to cobalt and vinylidene chloride and link distinct human signatures (SBS19 and SBS42) with 1,2,3-trichloropropane, a haloalkane and pollutant of drinking water, and find these and other signatures in human tumor genomes. We define the cross-species genomic landscape of tumors induced by an important compendium of agents with relevance to human health.

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Fig. 1: The landscape of mSBS signatures induced by chemical exposures and endogenous mutagenic processes.
Fig. 2: Transcriptional strand bias and replication timing of mutations in mouse lung, liver, kidney and forestomach tumors.
Fig. 3: Doublet/dinucleotide base substitution and indel signatures.
Fig. 4: Driver genes, the association between specific hotspot mutations and SBS signatures, and CNV profiles.
Fig. 5: Identification of human tumors with signatures related to mSBS19, mSBS42, mSBS_N1 and mSBS_N2.

Data availability

The raw sequencing data are available for download from the European Nucleotide Archive under accession nos. ERP021985 (lung tumor sequence data), ERP104478 and ERP106735 (liver tumor sequence data), ERP110807 (liver tumor methylation data), ERP106734 (kidney tumor sequence data), ERP115196 (stomach tumor sequence data). All other data are available in the supplementary tables and in the source data file. Source data are provided with this paper.

Code availability

The code used in this study is available at


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This work was funded by grants to D.J.A. from the Wellcome Trust, Cancer Research UK (with A.B.), the CRUK-funded Mutographs Project Work Package 3 (to A.B.) and the European Research Council under the European Union’s Seventh Framework Programme (no. FP7/2007–2013)/ERC synergy grant agreement no. 319661 COMBATCANCER. A.B. acknowledges support from National Cancer Institute grant no. R35CA210018. A.D. is supported by a UKRI Fellowship (no. MR/S00386X/1). We acknowledge the ENCODE Consortium and the ENCODE production laboratories. We acknowledge the support provided by the staff at the NTP tissue archives for this study. We thank M. Stratton, A. Boot, S. Rozen, S. Jackson and D. Phillips for helpful discussions.

Author information

Authors and Affiliations



The study was conceived by A.B. and R.C.S. R.C.S., A.B. and D.J.A. designed and supervised the project. Tumors were collected/generated by A.R.P., R.A.H., R.C.S. and the NTP. Computational analyses were performed by L.R., Y.R.L., A.D., M.A.Q., P.J.C., V.I., R.S. and L.B.A. Histopathology evaluation and sequencing were performed by A.R.P. and J.H., respectively. The manuscript was written by L.R. and D.J.A. with contributions from all other authors.

Corresponding authors

Correspondence to Allan Balmain or David J. Adams.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The comparative landscape of spontaneous and chemically induced tumours with genomic features.

a, Comparison of the colocalization of substitutions with histone marks and open chromatin in spontaneous and chemically induced tumours. Each point is a single replicate (for the induced these points are aggregated across multiple chemicals). For each point, we plot the observed/expected data from the MutationalPatterns software. The box plots show the Tukey statistics: The box shows the 1st—3rd quartiles, with a line at the median. The whiskers extend from the 1st and 3rd quartiles to the largest value no more than 1.5*IQR from the relevant quartile (See Source Data for sample numbers in each comparison). b, Table reporting the adjusted P values for the comparisons in a. A two-sided Mann-Whitney U-test was used to calculate the false-discovery rate corrected P values. c, Signatures identified using sigProfiler in the pentanucleotide context.

Extended Data Fig. 2 Strand coordinated clustering along the genome.

a, a liver tumour from a mouse exposed to TCP and b, a lung tumour from a mouse exposed to Isobutyl Nitrite.

Extended Data Fig. 3 Hierarchical clustering of the contribution of mSBS signatures across the collection of lung, liver, kidney and forestomach tumours sequenced in this study.

The profile of mutational signatures across the tumour collection. The signature profiles are shown in Fig. 1.

Extended Data Fig. 4 The landscape of Mouse Doublet Base Substitution (mDBS) Signatures induced by chemical exposures and endogenous mutagenic processes.

a, The catalogue of mouse doublet base substitution (mDBS) signatures. mDBS_N1 and mDBS_N2 are new DBS signatures. b, Number of mutations for each mDBS signature across the collection of lung, liver, kidney and forestomach tumours. Component 0 accounts for very few mutations and represents background mutations. c, The DBS spectrum obtained by normalizing and averaging the DBS spectra of the six lung tumours exposed to cobalt. This profile is almost identical to mDBS_N2.

Extended Data Fig. 5 The catalogue of mouse indel substitution (mID) signatures.

Shown are the indel signatures that were computed from the whole genome sequence data generated in this study.

Extended Data Fig. 6 Hierarchical clustering of copy number variants across the tumour collection.

Copy number events were called as described in the Methods. Notable clustering for tumours from mice exposed to DE-71 and vinylidene chloride are shown. The scale indicates absolute copy number.

Extended Data Fig. 7 Structural variants in spontaneous and chemical induced tumours.

a, Structural variants of two lung tumours showing chromothripsis and two liver tumours with many inversion events. b, Structural variants in the other samples (excluding the samples in a) across the collection of lung, liver, kidney and forestomach tumours.

Extended Data Fig. 8 Comparison of signatures computed with HDP to those computed with SigProfiler with 6 components (default result).

Shown are the signatures identified using HDP and corresponding signatures identified using the SigProfiler algorithm. For this comparison SigProfiler was run with 6 components.

Extended Data Fig. 9 Comparison of signatures computed with HDP to those computed with SigProfiler with 9 components.

Shown are the signatures identified using HDP and corresponding signatures identified using the SigProfiler algorithm. For this comparison SigProfiler was used with 9 components.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12

Source data

Source Data Fig. 1a

Statistical source data

Source Data Fig. 3a

Statistical source data

Source Data Fig. 3c

Statistical source data

Source Data Fig. 4c

Statistical source data

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Riva, L., Pandiri, A.R., Li, Y.R. et al. The mutational signature profile of known and suspected human carcinogens in mice. Nat Genet 52, 1189–1197 (2020).

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