Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Mutational signatures: emerging concepts, caveats and clinical applications

Abstract

Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient’s cancer, we have been able to discern generic patterns of mutations, termed ‘mutational signatures’, that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Conceptual developments and visualization of mutational signatures.
Fig. 2: Mechanisms of signature generation.
Fig. 3: Challenges associated with mutational signature frameworks.
Fig. 4: An update on the concept of mutational signatures.
Fig. 5: Dynamics of mutational signatures over cancer evolutionary time.
Fig. 6: Interplay and utility of experimental validation and cancer data analysis.

Similar content being viewed by others

References

  1. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012). This study presents catalogues of somatic mutations from 21 breast cancers, the respective mutational signatures of which were extracted by mathematical methods.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013). This study reports 21 distinct mutational signatures extracted from several cancer types, which form the basis of COSMIC mutational signatures v2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020). This study reports the largest number of mutational signatures to date, which form the basis of COSMIC mutational signatures v3, and introduces DBSs and IDs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Degasperi, A. et al. A practical framework and online tool for mutational signature analyses show inter-tissue variation and driver dependencies. Nat. Cancer 1, 249–263 (2020). This study introduces a practical framework and Signal, an online tool, to analyse mutational signatures. It also reports evidence of tissue-specific variability in mutational signatures, which may impact tumour classification and clinical application.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pich, O. et al. The mutational footprints of cancer therapies. Nat. Genet. 51, 1732–1740 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Baez-Ortega, A. & Gori, K. Computational approaches for discovery of mutational signatures in cancer. Brief. Bioinforma. 20, 77–88 (2019).

    Article  CAS  Google Scholar 

  11. Omichessan, H., Severi, G. & Perduca, V. Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance. PLoS ONE 14, e0221235 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Koh, G., Zou, X. & Nik-Zainal, S. Mutational signatures: experimental design and analytical framework. Genome Biol. 21, 37 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836.e16 (2019). This is the largest and most comprehensive screen of environmental mutagen-associated mutational signatures published to date.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Fantini, D., Vidimar, V., Yu, Y., Condello, S. & Meeks, J. J. MutSignatures: an R package for extraction and analysis of cancer mutational signatures. Sci. Rep. 10, 18217 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cartolano, M. et al. CaMuS: simultaneous fitting and de novo imputation of cancer mutational signature. Sci. Rep. 10, 19316 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013). This article describes the use of non-negative matrix factorization to extract mutational signatures.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Petljak, M. et al. Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis. Cell 176, 1282–1294.e20 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Helleday, T., Eshtad, S. & Nik-Zainal, S. Mechanisms underlying mutational signatures in human cancers. Nat. Rev. Genet. 15, 585–598 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Davies, H. et al. Whole-genome sequencing reveals breast cancers with mismatch repair deficiency. Cancer Res. 77, 4755–4762 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Lee-Six, H. et al. The landscape of somatic mutation in normal colorectal epithelial cells. Nature 574, 532–537 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016). This study presents the first RSs and introduces a framework to classify these.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Letouze, E. et al. Mutational signatures reveal the dynamic interplay of risk factors and cellular processes during liver tumorigenesis. Nat. Commun. 8, 1315 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Hillman, R. T., Chisholm, G. B., Lu, K. H. & Futreal, P. A. Genomic rearrangement signatures and clinical outcomes in high-grade serous ovarian cancer. J. Natl Cancer Inst. 110, 265–272 (2018).

    Article  CAS  Google Scholar 

  27. Kamp, J. A., van Schendel, R., Dilweg, I. W. & Tijsterman, M. BRCA1-associated structural variations are a consequence of polymerase theta-mediated end-joining. Nat. Commun. 11, 3615 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mateos-Gomez, P. A. et al. Mammalian polymerase theta promotes alternative NHEJ and suppresses recombination. Nature 518, 254–257 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ceccaldi, R. et al. Homologous-recombination-deficient tumours are dependent on Poltheta-mediated repair. Nature 518, 258–262 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bayard, Q. et al. Cyclin A2/E1 activation defines a hepatocellular carcinoma subclass with a rearrangement signature of replication stress. Nat. Commun. 9, 5235 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang, S. et al. Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes. PLoS Genet. 17, e1009557 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Steele, C. D. et al. Undifferentiated sarcomas develop through distinct evolutionary pathways. Cancer Cell 35, 441–456 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lindahl, T. An N-glycosidase from Escherichia coli that releases free uracil from DNA containing deaminated cytosine residues. Proc. Natl Acad. Sci. USA 71, 3649–3653 (1974).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Krokan, H. E. & Bjoras, M. Base excision repair. Cold Spring Harb. Perspect. Biol. 5, a012583 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Strauss, B. S. The “A” rule revisited: polymerases as determinants of mutational specificity. DNA Repair 1, 125–135 (2002).

    Article  CAS  PubMed  Google Scholar 

  38. Maura, F. et al. A practical guide for mutational signature analysis in hematological malignancies. Nat. Commun. 10, 2969 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Rosales, R. A., Drummond, R. D., Valieris, R., Dias-Neto, E. & da Silva, I. T. signeR: an empirical Bayesian approach to mutational signature discovery. Bioinformatics 33, 8–16 (2017).

    Article  CAS  PubMed  Google Scholar 

  40. Fischer, A., Illingworth, C. J., Campbell, P. J. & Mustonen, V. EMu: probabilistic inference of mutational processes and their localization in the cancer genome. Genome Biol. 14, R39 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Kasar, S. et al. Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nat. Commun. 6, 8866 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Campbell, B. B. et al. Comprehensive analysis of hypermutation in human cancer. Cell 171, 1042–1056.e10 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Shen, J. C., Rideout, W. M. 3rd & Jones, P. A. High frequency mutagenesis by a DNA methyltransferase. Cell 71, 1073–1080 (1992).

    Article  CAS  PubMed  Google Scholar 

  45. Pfeifer, G. P. Mutagenesis at methylated CpG sequences. Curr. Top. Microbiol. 301, 259–281 (2006).

    CAS  Google Scholar 

  46. Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lugli, N. et al. Enhanced rate of acquisition of point mutations in mouse intestinal adenomas compared to normal tissue. Cell Rep. 19, 2185–2192 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Dulak, A. M. et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat. Genet. 45, 478–486 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Angus, L. et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat. Genet. 51, 1450–1458 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. De Mattos-Arruda, L. et al. The genomic and immune landscapes of lethal metastatic breast cancer. Cell Rep. 27, 2690–2708.e10 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Christensen, S. et al. 5-Fluorouracil treatment induces characteristic T>G mutations in human cancer. Nat. Commun. 10, 4571 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Tomkova, M. et al. Deciphering the causes of the COSMIC mutational signature 17 by combining pan-cancer data with experimental mouse models [abstract]. Cancer Res. 79, 4661 (2019).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Dvorak, K. et al. Bile acids in combination with low pH induce oxidative stress and oxidative DNA damage: relevance to the pathogenesis of Barrett’s oesophagus. Gut 56, 763–771 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Inoue, M. et al. Induction of chromosomal gene mutations in Escherichia coli by direct incorporation of oxidatively damaged nucleotides. New evaluation method for mutagenesis by damaged DNA precursors in vivo. J. Biol. Chem. 273, 11069–11074 (1998).

    Article  CAS  PubMed  Google Scholar 

  57. Viel, A. et al. A specific mutational signature associated with DNA 8-oxoguanine persistence in MUTYH-defective colorectal cancer. EBioMedicine 20, 39–49 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Pilati, C. et al. Mutational signature analysis identifies MUTYH deficiency in colorectal cancers and adrenocortical carcinomas. J. Pathol. 242, 10–15 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Zou, X. Q. et al. A systematic CRISPR screen defines mutational mechanisms underpinning signatures caused by replication errors and endogenous DNA damage. Nat. Cancer 2, 643–657 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kuijk, E. et al. The mutational impact of culturing human pluripotent and adult stem cells. Nat. Commun. 11, 2493 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rouhani, F. J. et al. Mutational history of a human cell lineage from somatic to induced pluripotent stem cells. PLoS Genet. 12, e1005932 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Brunner, S. F. et al. Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature 574, 538–542 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Moore, L. et al. The mutational landscape of normal human endometrial epithelium. Nature 580, 640–646 (2020).

    Article  CAS  PubMed  Google Scholar 

  66. Lawson, A. R. J. et al. Extensive heterogeneity in somatic mutation and selection in the human bladder. Science 370, 75–82 (2020).

    Article  CAS  PubMed  Google Scholar 

  67. Yoshida, K. et al. Tobacco smoking and somatic mutations in human bronchial epithelium. Nature 578, 266–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. D’Antonio, M. et al. Insights into the mutational burden of human induced pluripotent stem cells from an integrative multi-omics approach. Cell Rep. 24, 883–894 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Rouhani, F. J. et al. Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells. Preprint at bioRxiv https://doi.org/10.1101/2021.02.04.429731 (2021).

  70. Nik-Zainal, S. & Hall, B. A. Cellular survival over genomic perfection. Science 366, 802–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Yates, L. R. et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 32, 169–184.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Naxerova, K. et al. Origins of lymphatic and distant metastases in human colorectal cancer. Science 357, 55–60 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yaeger, R. et al. Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33, 125–136.e3 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Liu, D. et al. Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer. Nat. Commun. 8, 2193 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Swanton, C., McGranahan, N., Starrett, G. J. & Harris, R. S. APOBEC enzymes: mutagenic fuel for cancer evolution and heterogeneity. Cancer Discov. 5, 704–712 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lefebvre, C. et al. Mutational profile of metastatic breast cancers: a retrospective analysis. PLoS Med. 13, e1002201 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Pleasance, E. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat. Cancer 1, 452–468 (2020).

    Article  PubMed  Google Scholar 

  79. Mendelaar, P. A. J. et al. Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features. Nat. Commun. 12, 574 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Rubanova, Y. et al. Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nat. Commun. 11, 731 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Besaratinia, A. & Pfeifer, G. P. Applications of the human p53 knock-in (Hupki) mouse model for human carcinogen testing. FASEB J. 24, 2612–2619 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Liu, Z. et al. Human tumor p53 mutations are selected for in mouse embryonic fibroblasts harboring a humanized p53 gene. Proc. Natl Acad. Sci. USA 101, 2963–2968 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Szikriszt, B. et al. A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome Biol. 17, 99 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Meier, B. et al. C. elegans whole-genome sequencing reveals mutational signatures related to carcinogens and DNA repair deficiency. Genome Res. 24, 1624–1636 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Volkova, N. V. et al. Mutational signatures are jointly shaped by DNA damage and repair. Nat. Commun. 11, 2169 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Boot, A. et al. In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors. Genome Res. 28, 654–665 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Pleguezuelos-Manzano, C. et al. Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli. Nature 580, 269–273 (2020).

    Article  CAS  PubMed  Google Scholar 

  90. Dziubanska-Kusibab, P. J. et al. Colibactin DNA-damage signature indicates mutational impact in colorectal cancer. Nat. Med. 26, 1063–1069 (2020).

    Article  CAS  PubMed  Google Scholar 

  91. Boot, A. et al. Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types. Genome Res. 30, 803–813 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Zou, X. et al. Validating the concept of mutational signatures with isogenic cell models. Nat. Commun. 9, 1744 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Jager, M. et al. Deficiency of nucleotide excision repair is associated with mutational signature observed in cancer. Genome Res. 29, 1067–1077 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Drost, J. et al. Use of CRISPR-modified human stem cell organoids to study the origin of mutational signatures in cancer. Science 358, 234–238 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483–1489 (2015).

    Article  CAS  PubMed  Google Scholar 

  96. Bryant, H. E. et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434, 913–917 (2005).

    Article  CAS  PubMed  Google Scholar 

  97. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    Article  CAS  PubMed  Google Scholar 

  98. Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).

    Article  CAS  PubMed  Google Scholar 

  99. Telli, M. L. et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin. Cancer Res. 22, 3764–3773 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Abkevich, V. et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br. J. Cancer 107, 1776–1782 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Birkbak, N. J. et al. Telomeric allelic imbalance indicates defective DNA repair and sensitivity to DNA-damaging agents. Cancer Discov. 2, 366–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Popova, T. et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res. 72, 5454–5462 (2012).

    Article  CAS  PubMed  Google Scholar 

  103. Timms, K. M. et al. Association of BRCA1/2 defects with genomic scores predictive of DNA damage repair deficiency among breast cancer subtypes. Breast Cancer Res. 16, 475–483 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017). This study describes the first clinical predictive tool, HRDetect, designed using a panel of mutational signatures to predict HRD.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Zhao, E. Y. et al. Homologous recombination deficiency and platinum-based therapy outcomes in advanced breast cancer. Clin. Cancer Res. 23, 7521–7530 (2017).

    Article  CAS  PubMed  Google Scholar 

  106. Nones, K. et al. Whole-genome sequencing reveals clinically relevant insights into the aetiology of familial breast cancers. Ann. Oncol. 30, 1071–1079 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Nguyen, L., Martens, J. W. M., Van Hoeck, A. & Cuppen, E. Pan-cancer landscape of homologous recombination deficiency. Nat. Commun. 11, 5584 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Gulhan, D. C., Lee, J. J., Melloni, G. E. M., Cortes-Ciriano, I. & Park, P. J. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nat. Genet. 51, 912–919 (2019).

    Article  CAS  PubMed  Google Scholar 

  109. Staaf, J. et al. Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study. Nat. Med. 25, 1526–1533 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Chopra, N. et al. Homologous recombination DNA repair deficiency and PARP inhibition activity in primary triple negative breast cancer. Nat. Commun. 11, 2662 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Thibodeau, S. N., Bren, G. & Schaid, D. Microsatellite instability in cancer of the proximal colon. Science 260, 816–819 (1993).

    Article  CAS  PubMed  Google Scholar 

  112. Ionov, Y., Peinado, M. A., Malkhosyan, S., Shibata, D. & Perucho, M. Ubiquitous somatic mutations in simple repeated sequences reveal a new mechanism for colonic carcinogenesis. Nature 363, 558–561 (1993).

    Article  CAS  PubMed  Google Scholar 

  113. Kim, T. M., Laird, P. W. & Park, P. J. The landscape of microsatellite instability in colorectal and endometrial cancer genomes. Cell 155, 858–868 (2013).

    Article  CAS  PubMed  Google Scholar 

  114. Lynch, H. T., Snyder, C. L., Shaw, T. G., Heinen, C. D. & Hitchins, M. P. Milestones of Lynch syndrome: 1895–2015. Nat. Rev. Cancer 15, 181–194 (2015).

    Article  CAS  PubMed  Google Scholar 

  115. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Middha, S. et al. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis. Oncol. 1, 1–17 (2017).

    Google Scholar 

  119. Niu, B. F. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).

    Article  CAS  PubMed  Google Scholar 

  120. Salipante, S. J., Scroggins, S. M., Hampel, H. L., Turner, E. H. & Pritchard, C. C. Microsatellite instability detection by next generation sequencing. Clin. Chem. 60, 1192–1199 (2014).

    Article  CAS  PubMed  Google Scholar 

  121. Germano, G. et al. Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth. Nature 552, 116–120 (2017).

    Article  CAS  PubMed  Google Scholar 

  122. Lemery, S., Keegan, P. & Pazdur, R. First FDA approval agnostic of cancer site — when a biomarker defines the indication. N. Engl. J. Med. 377, 1409–1412 (2017).

    Article  PubMed  Google Scholar 

  123. Stelloo, E. et al. Practical guidance for mismatch repair-deficiency testing in endometrial cancer. Ann. Oncol. 28, 96–102 (2017).

    Article  CAS  PubMed  Google Scholar 

  124. Kawakami, H., Zaanan, A. & Sinicrope, F. A. Microsatellite instability testing and its role in the management of colorectal cancer. Curr. Treat. Options Oncol. 16, 30 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Buhard, O. et al. Multipopulation analysis of polymorphisms in five mononucleotide repeats used to determine the microsatellite instability status of human tumors. J. Clin. Oncol. 24, 241–251 (2006).

    Article  CAS  PubMed  Google Scholar 

  126. Huang, M. N. et al. MSIseq: software for assessing microsatellite instability from catalogs of somatic mutations. Sci. Rep. 5, 13321 (2015).

    Article  CAS  PubMed  Google Scholar 

  127. Fabrizio, D. A. et al. Beyond microsatellite testing: assessment of tumor mutational burden identifies subsets of colorectal cancer who may respond to immune checkpoint inhibition. J. Gastrointest. Oncol. 9, 610–617 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Schrock, A. B. et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann. Oncol. 30, 1096–1103 (2019).

    Article  CAS  PubMed  Google Scholar 

  129. Mehnert, J. M. et al. Immune activation and response to pembrolizumab in POLE-mutant endometrial cancer. J. Clin. Invest. 126, 2334–2340 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Howitt, B. E. et al. Association of polymerase e-mutated and microsatellite-instable endometrial cancers with neoantigen load, number of tumor-infiltrating lymphocytes, and expression of PD-1 and PD-L1. JAMA Oncol. 1, 1319–1323 (2015).

    Article  PubMed  Google Scholar 

  131. Johanns, T. M. et al. Immunogenomics of hypermutated glioblastoma: a patient with germline POLE deficiency treated with checkpoint blockade immunotherapy. Cancer Discov. 6, 1230–1236 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Momen, S. et al. Dramatic response of metastatic cutaneous angiosarcoma to an immune checkpoint inhibitor in a patient with xeroderma pigmentosum: whole-genome sequencing aids treatment decision in end-stage disease. Cold Spring Harb. Mol. Case Stud. 5, a004408 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Chung, J. et al. DNA polymerase and mismatch repair exert distinct microsatellite instability signatures in normal and malignant human cells. Cancer Discov. 11, 1176–1191 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Roberts, S. A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Nik-Zainal, S. et al. Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer. Nat. Genet. 46, 487–491 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Starrett, G. J. et al. The DNA cytosine deaminase APOBEC3H haplotype I likely contributes to breast and lung cancer mutagenesis. Nat. Commun. 7, 12918 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Middlebrooks, C. D. et al. Association of germline variants in the APOBEC3 region with cancer risk and enrichment with APOBEC-signature mutations in tumors. Nat. Genet. 48, 1330–1338 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    Article  CAS  PubMed  Google Scholar 

  139. Walker, B. A. et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat. Commun. 6, 6997 (2015).

    Article  CAS  PubMed  Google Scholar 

  140. Wang, S. X., Jia, M. M., He, Z. K. & Liu, X. S. APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer. Oncogene 37, 3924–3936 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Boichard, A., Tsigelny, I. F. & Kurzrock, R. High expression of PD-1 ligands is associated with kataegis mutational signature and APOBEC3 alterations. Oncoimmunology 6, e1284719 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Gibney, G. T., Weiner, L. M. & Atkins, M. B. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 17, e542–e551 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Law, E. K. et al. The DNA cytosine deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast cancer. Sci. Adv. 2, e1601737 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Menghi, F. et al. The tandem duplicator phenotype as a distinct genomic configuration in cancer. Proc. Natl Acad. Sci. USA 113, E2373–E2382 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Menghi, F. et al. The tandem duplicator phenotype is a prevalent genome-wide cancer configuration driven by distinct gene mutations. Cancer Cell 34, 197–210.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Willis, N. A. et al. Mechanism of tandem duplication formation in BRCA1-mutant cells. Nature 551, 590–595 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Popova, T. et al. Ovarian cancers harboring inactivating mutations in CDK12 display a distinct genomic instability pattern characterized by large tandem duplications. Cancer Res. 76, 1882–1891 (2016).

    Article  CAS  PubMed  Google Scholar 

  148. Macheret, M. & Halazonetis, T. D. Intragenic origins due to short G1 phases underlie oncogene-induced DNA replication stress. Nature 555, 112–116 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Glodzik, D. et al. A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers. Nat. Genet. 49, 341–348 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Quigley, D. A. et al. Genomic hallmarks and structural variation in metastatic prostate cancer. Cell 174, 758–769.e9 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Stephens, P. J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400–404 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Schimmel, J., Kool, H., van Schendel, R. & Tijsterman, M. Mutational signatures of non-homologous and polymerase theta-mediated end-joining in embryonic stem cells. EMBO J. 36, 3634–3649 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Wyatt, D. W. et al. Essential roles for polymerase theta-mediated end joining in the repair of chromosome breaks. Mol. Cell 63, 662–673 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Higgins, G. S. et al. A small interfering RNA screen of genes involved in DNA repair identifies tumor-specific radiosensitization by POLQ knockdown. Cancer Res. 70, 2984–2993 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Yousefzadeh, M. J. et al. Mechanism of suppression of chromosomal instability by DNA polymerase POLQ. PLoS Genet. 10, e1004654 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  156. Wang, Z. et al. DNA polymerase (POLQ) is important for repair of DNA double-strand breaks caused by fork collapse. J. Biol. Chem. 294, 3909–3919 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).

    Article  CAS  PubMed  Google Scholar 

  158. Maciejowski, J., Li, Y., Bosco, N., Campbell, P. J. & de Lange, T. Chromothripsis and kataegis induced by telomere crisis. Cell 163, 1641–1654 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Umbreit, N. T. et al. Mechanisms generating cancer genome complexity from a single cell division error. Science 368, 282–294 (2020).

    Article  CAS  Google Scholar 

  161. Shoshani, O. et al. Chromothripsis drives the evolution of gene amplification in cancer. Nature 591, 137–141 (2021).

    Article  CAS  PubMed  Google Scholar 

  162. Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  163. Driscoll, C. B. et al. APOBEC3B-mediated corruption of the tumor cell immunopeptidome induces heteroclitic neoepitopes for cancer immunotherapy. Nat. Commun. 11, 790 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Roudko, V. et al. Shared immunogenic poly-epitope frameshift mutations in microsatellite unstable tumors. Cell 183, 1634–1649.e17 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Koster, J. & Plasterk, R. H. A. A library of neo open reading frame peptides (NOPs) as a sustainable resource of common neoantigens in up to 50% of cancer patients. Sci. Rep. 9, 6577 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  166. Diaz, L. A. Jr & Bardelli, A. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32, 579–586 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Alix-Panabieres, C. & Pantel, K. Clinical applications of circulating tumor cells and circulating tumor dna as liquid biopsy. Cancer Discov. 6, 479–491 (2016).

    Article  CAS  PubMed  Google Scholar 

  168. Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Annala, M. et al. Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer. Cancer Discov. 8, 444–457 (2018).

    Article  CAS  PubMed  Google Scholar 

  170. Dawson, S. J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

    Article  CAS  PubMed  Google Scholar 

  171. Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013).

    Article  CAS  PubMed  Google Scholar 

  172. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Zviran, A. et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat. Med. 26, 1114–1124 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Turnbull, C. et al. The 100 000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361, k1687 (2018).

    Article  PubMed  Google Scholar 

  175. Weill Cornell Medicine. Weill Cornell Medicine, NewYork-Presbyterian Hospital, and Illumina collaborate on scalable clinical whole-genome sequencing initiative. EurekAlert https://www.eurekalert.org/pub_releases/2020-12/wcm-wcm120220.php (2020).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded by the Cancer Research UK (CRUK) Advanced Clinician Scientist Award (C60100/A23916), the Dr. Josef Steiner Cancer Research Award 2019, a Medical Research Council (MRC) Grant-in-Aid to the MRC Cancer Unit, the CRUK Pioneer Award, a Wellcome Strategic Award (WT101126), Basser Gray Prime Award 2020, and supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014).

Author information

Authors and Affiliations

Authors

Contributions

S.N.-Z., G.K., A.D. and S.M. researched data for the article. S.N.-Z., G.K., A.D. and X.Z. contributed to discussion of content and writing the article. S.N.-Z., G.K. and X.Z. reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Serena Nik-Zainal.

Ethics declarations

Competing interests

S.N.-Z. holds patents on clinical algorithms of mutational signatures and, during the completion of the manuscript, also had advisory roles for AstraZeneca, Artios Pharma Ltd and the Scottish Genome Project. The other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Cancer thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

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

Related links

Beyond 1 Million Genomes (B1MG): https://b1mg-project.eu

Signal: a Web-based tool for cancer and experimentally generated mutational signature exploration and analysis: https://signal.mutationalsignatures.com

Wellcome Trust Sanger Institute COSMIC signature resource: https://cancer.sanger.ac.uk/cosmic/signatures

Supplementary information

Glossary

Homologous recombination deficiency

(HRD). An inability to repair DNA double-strand breaks via homologous recombination-based repair mechanisms, typically caused by germ line or somatic BRCA gene mutations.

Poly(dA:dT)

Homopolymeric stretches of deoxyadenosine (dA) or deoxythymidine (dT) nucleotides on one strand of double-stranded DNA. They are overabundant in eukaryotic genomes and constitute a hotspot of mutagenesis.

Non-negative matrix factorization framework

An unsupervised machine learning framework in which a matrix is factorized into (usually) two matrices, with the property that all three matrices have no negative elements, thus allowing us to model a data matrix as linear combinations of a set of basis vectors (building blocks).

Hierarchical Dirichlet process

A non-parametric Bayesian approach to clustering grouped data.

Local n-jumps

A cluster of n structural variants in a single genomic region, usually phased to a single derivative chromosome, exhibiting some copy number gains and junctions with inverted and non-inverted orientation.

Chromoplexy

Large chained and weaved genomic rearrangements that involves multiple chromosomes.

Template switching

A recombination-based mechanism in which stalled polymerase finds an alternative template such that it can ‘borrow’ to get around a DNA lesion on the damaged parent strand and restart replication. This is most commonly the newly synthesized daughter strand on the sister chromatid or other sequences with homology to the single-stranded DNA region.

Matrix decomposition

Also called matrix factorization, works by decomposing the user-item interaction matrix into a product of two lower-dimensionality matrices, such as \(M\approx S\times E\), where M is the catalogue matrix, with mutation types as rows and samples as columns, S is the signature matrix, with mutation types as rows and signatures as columns, and E is the exposure matrix, with signatures as rows and samples as columns.

Strauss’s A rule

The preferential incorporation of adenine opposite a non-instructional DNA blocking lesion or across an abasic site.

Replication stress

The slowing or stalling of replication fork progression and/or DNA synthesis in response to DNA damage or any hindrance to DNA replication.

8-Oxo-dGTP

8-Oxo-2′-deoxyguanosine 5′-triphosphate, the oxidized form of 2′-deoxyguanosine 5′-triphosphate (dGTP). It can mispair with A, leading to C>A/G>T transversion mutations.

Synthetic lethality

Interaction between two genes when the perturbation of either gene alone is viable but the perturbation of either gene results in cell death.

Microsatellite instability

(MSI). Variability in the length of base pair repeated sequences (less than 5 bp) due to short insertions/deletions caused by replication slippage and that is normally kept stable by mismatch repair.

Kataegis

A base substitution hypermutation that comprises C·G → T·A transitions and C·G → G·C transversions with a predilection for a thymine preceding the mutated cytosine (that is, a TpC context); it usually macroscopically colocalizes with structural variation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koh, G., Degasperi, A., Zou, X. et al. Mutational signatures: emerging concepts, caveats and clinical applications. Nat Rev Cancer 21, 619–637 (2021). https://doi.org/10.1038/s41568-021-00377-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41568-021-00377-7

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer