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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


  1. 1.

    Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

    CAS  Article  Google Scholar 

  2. 2.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Kemp, C. J. Animal models of chemical carcinogenesis: driving breakthroughs in cancer research for 100 years. Cold Spring Harb. Protoc. 2015, 865–874 (2015).

    Article  Google Scholar 

  4. 4.

    Bucher, J. R. The National Toxicology Program rodent bioassay: designs, interpretations, and scientific contributions. Ann. N. Y. Acad. Sci. 982, 198–207 (2002).

    CAS  Article  Google Scholar 

  5. 5.

    Innes, J. R. et al. Bioassay of pesticides and industrial chemicals for tumorigenicity in mice: a preliminary note. J. Natl Cancer Inst. 42, 1101–1114 (1969).

    CAS  PubMed  Google Scholar 

  6. 6.

    Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    CAS  Article  Google Scholar 

  7. 7.

    Maronpot, R. R. Biological basis of differential susceptibility to hepatocarcinogenesis among mouse strains. J. Toxicol. Pathol. 22, 11–33 (2009).

    CAS  Article  Google Scholar 

  8. 8.

    Ames, B. N., Durston, W. E., Yamasaki, E. & Lee, F. D. Carcinogens are mutagens: a simple test system combining liver homogenates for activation and bacteria for detection. Proc. Natl Acad. Sci. USA 70, 2281–2285 (1973).

    CAS  Article  Google Scholar 

  9. 9.

    Mimaki, S. et al. Hypermutation and unique mutational signatures of occupational cholangiocarcinoma in printing workers exposed to haloalkanes. Carcinogenesis 37, 817–826 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

    CAS  Article  Google Scholar 

  11. 11.

    Olivier, M. et al. Modelling mutational landscapes of human cancers in vitro. Sci. Rep. 4, 4482 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Nik-Zainal, S. et al. The genome as a record of environmental exposure. Mutagenesis 30, 763–770 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Renaud, H. J., Cui, J. Y., Khan, M. & Klaassen, C. D. Tissue distribution and gender-divergent expression of 78 cytochrome P450 mRNAs in mice. Toxicol. Sci. 124, 261–277 (2011).

    CAS  Article  Google Scholar 

  14. 14.

    Vinylidene chloride. IARC Monogr. Eval. Carcinog. Risks Hum. 71, 1163–1180 (1999).

  15. 15.

    Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Woodfine, K. et al. Replication timing of the human genome. Hum. Mol. Genet. 13, 191–202 (2004).

    CAS  Article  Google Scholar 

  17. 17.

    Haradhvala, N. J. et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell 164, 538–549 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nat. Genet. 41, 393–395 (2009).

    CAS  Article  Google Scholar 

  19. 19.

    Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Kazanov, M. D. et al. APOBEC-induced cancer mutations are uniquely enriched in early-replicating, gene-dense, and active chromatin regions. Cell Rep. 13, 1103–1109 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Rebhandl, S., Huemer, M., Greil, R. & Geisberger, R. AID/APOBEC deaminases and cancer. Oncoscience 2, 320–333 (2015).

    Article  Google Scholar 

  22. 22.

    Lison, D., van den Brule, S. & Van Maele-Fabry, G. Cobalt and its compounds: update on genotoxic and carcinogenic activities. Crit. Rev. Toxicol. 48, 522–539 (2018).

    CAS  Article  Google Scholar 

  23. 23.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS  Article  Google Scholar 

  24. 24.

    Huang, X., Wojtowicz, D. & Przytycka, T. M. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics 34, 330–337 (2018).

    CAS  Article  Google Scholar 

  25. 25.

    Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836.e16 (2019).

    CAS  Article  Google Scholar 

  26. 26.

    Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851 (2014).

    CAS  Article  Google Scholar 

  27. 27.

    Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinformatics 56, 15.10.1–15.10.18 (2016).

    Article  Google Scholar 

  28. 28.

    Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinformatics 52, 15.7.1–15.7.12 (2015).

    Article  Google Scholar 

  29. 29.

    Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).

    Article  Google Scholar 

  30. 30.

    Ramazzotti, D., Lal, A., Liu, K., Tibshirani, R. & Sidow, A. De novo mutational signature discovery in tumour genomes with SparesSignatures. Preprint at bioRxiv (2019).

  31. 31.

    Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    Morganella, S. et al. The topography of mutational processes in breast cancer genomes. Nat. Commun. 7, 11383 (2016).

    CAS  Article  Google Scholar 

  33. 33.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  Article  Google Scholar 

  34. 34.

    Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  Google Scholar 

  35. 35.

    Tate, J. G. et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    CAS  Article  Google Scholar 

  36. 36.

    Oesper, L., Satas, G. & Raphael, B. J. Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data. Bioinformatics 30, 3532–3540 (2014).

    CAS  Article  Google Scholar 

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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




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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