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.

The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies

Abstract

The whole-genome sequencing of prospectively collected tissue biopsies from 442 patients with metastatic breast cancer reveals that, compared to primary breast cancer, tumor mutational burden doubles, the relative contributions of mutational signatures shift and the mutation frequency of six known driver genes increases in metastatic breast cancer. Significant associations with pretreatment are also observed. The contribution of mutational signature 17 is significantly enriched in patients pretreated with fluorouracil, taxanes, platinum and/or eribulin, whereas the de novo mutational signature I identified in this study is significantly associated with pretreatment containing platinum-based chemotherapy. Clinically relevant subgroups of tumors are identified, exhibiting either homologous recombination deficiency (13%), high tumor mutational burden (11%) or specific alterations (24%) linked to sensitivity to FDA-approved drugs. This study provides insights into the biology of metastatic breast cancer and identifies clinically useful genomic features for the future improvement of patient management.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Overview of the study design and biopsy sites.
Fig. 2: De novo signature I is associated with prior platinum-based chemotherapy.
Fig. 3: Mutational signatures: metastatic breast cancer versus primary breast cancer.
Fig. 4: Unsupervised clustering reveals distinct genomic phenotypes in metastatic breast cancer.
Fig. 5: Driver genes in metastatic breast cancer versus primary breast cancer.
Fig. 6: Actionability.

Data availability

The WGS and corresponding clinical data were requested from the Hartwig Medical Foundation and provided under data request no. DR-026. The clinical data provided by the CPCT were locked on 1 June 2018. Both WGS and clinical data are freely available for academic use from the Hartwig Medical Foundation through standardized procedures; request forms can be found at https://www.hartwigmedicalfoundation.nl27.

Code availability

Full code is available at https://github.com/hartwigmedical/ and https://bitbucket.org/ccbc/r2ccbc.

References

  1. 1.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 68, 7–30 (2018).

    Article  PubMed Central  Google Scholar 

  2. 2.

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

    CAS  Article  PubMed Central  Google Scholar 

  3. 3.

    Nik-Zainal, S. & Morganella, S. Mutational signatures in breast cancer: the problem at the DNA level. Clin. Cancer Res. 23, 2617–2629 (2017).

    Article  PubMed Central  Google Scholar 

  4. 4.

    Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    CAS  Article  Google Scholar 

  5. 5.

    Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    CAS  Article  PubMed Central  Google Scholar 

  6. 6.

    Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    CAS  Article  PubMed Central  Google Scholar 

  7. 7.

    Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  8. 8.

    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).

    CAS  Article  PubMed Central  Google Scholar 

  9. 9.

    Burns, M. B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013).

    CAS  Article  PubMed Central  Google Scholar 

  10. 10.

    Brown, D. et al. Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Nat. Commun. 8, 14944 (2017).

    Article  PubMed Central  Google Scholar 

  11. 11.

    Brastianos, P. K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).

    CAS  Article  PubMed Central  Google Scholar 

  12. 12.

    Savas, P. et al. The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”. PLoS Med. 13, e1002204 (2016).

    Article  PubMed Central  Google Scholar 

  13. 13.

    Fumagalli, D. et al. Somatic mutation, copy number and transcriptomic profiles of primary and matched metastatic estrogen receptor-positive breast cancers. Ann. Oncol. 27, 1860–1866 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Ng, C. K. Y. et al. Genetic heterogeneity in therapy-naïve synchronous primary breast cancers and their metastases. Clin. Cancer Res. 23, 4402–4415 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  15. 15.

    Schrijver, W. A. M. E. et al. Mutation profiling of key cancer genes in primary breast cancers and their distant metastases. Cancer Res. 78, 3112–3121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

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

    Article  PubMed Central  Google Scholar 

  17. 17.

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

    CAS  Article  PubMed Central  Google Scholar 

  18. 18.

    Razavi, P. et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell 34, 427–438.e6 (2018).

    CAS  Article  PubMed Central  Google Scholar 

  19. 19.

    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).

    CAS  Article  PubMed Central  Google Scholar 

  20. 20.

    Wyatt, M. D. & Wilson, D. M. 3rd Participation of DNA repair in the response to 5-fluorouracil. Cell. Mol. Life Sci. 66, 788–799 (2009).

    CAS  Article  PubMed Central  Google Scholar 

  21. 21.

    Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 173, 1823 (2018).

    CAS  Article  PubMed Central  Google Scholar 

  22. 22.

    Ramalingam, S. S. et al. Abstract CT078: Tumor mutational burden (TMB) as a biomarker for clinical benefit from dual immune checkpoint blockade with nivolumab (nivo) + ipilimumab (ipi) in first-line (1L) non-small cell lung cancer (NSCLC): identification of TMB cutoff from CheckMate 568. Cancer Res. 78, CT078 (2018).

    Google Scholar 

  23. 23.

    Smid, M. et al. Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration. Nat. Commun. 7, 12910 (2016).

    CAS  Article  PubMed Central  Google Scholar 

  24. 24.

    Pitt, J. J. et al. Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features. Nat. Commun. 9, 4181 (2018).

    Article  PubMed Central  Google Scholar 

  25. 25.

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

    CAS  Article  PubMed Central  Google Scholar 

  26. 26.

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

    CAS  Article  PubMed Central  Google Scholar 

  27. 27.

    Priestley, P. et al. Pan-cancer whole genome analyses of metastatic solid tumors. Nature (in the press).

  28. 28.

    Brahmer, J. R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455–2465 (2012).

    CAS  Article  PubMed Central  Google Scholar 

  29. 29.

    Lord, C. J. & Ashworth, A. PARP inhibitors: synthetic lethality in the clinic. Science 355, 1152–1158 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  30. 30.

    Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis Oncol. https://ascopubs.org/doi/pdfdirect/10.1200/PO.17.00011 (2017).

  31. 31.

    Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    CAS  Article  PubMed Central  Google Scholar 

  32. 32.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  PubMed Central  Google Scholar 

  33. 33.

    Casper, J. et al. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res. 46, D762–D769 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–D783 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  35. 35.

    Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).

    Article  PubMed Central  Google Scholar 

  36. 36.

    Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  37. 37.

    Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed Central  Google Scholar 

  38. 38.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Article  PubMed Central  Google Scholar 

  39. 39.

    Spurdle, A. B. et al. ENIGMA—evidence-based network for the interpretation of germline mutant alleles: an international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes. Hum. Mutat. 33, 2–7 (2012).

    CAS  Article  PubMed Central  Google Scholar 

  40. 40.

    Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).

    CAS  Article  PubMed Central  Google Scholar 

  41. 41.

    Huber, W., Toedling, J. & Steinmetz, L. M. Transcript mapping with high-density oligonucleotide tiling arrays. Bioinformatics 22, 1963–1970 (2006).

    CAS  Article  PubMed Central  Google Scholar 

  42. 42.

    Gel, B. & Serra, E. karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics 33, 3088–3090 (2017).

    CAS  Article  PubMed Central  Google Scholar 

  43. 43.

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

    Article  PubMed Central  Google Scholar 

  44. 44.

    Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010).

    Article  PubMed Central  Google Scholar 

  45. 45.

    Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

    CAS  Article  Google Scholar 

  46. 46.

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

    CAS  Article  PubMed Central  Google Scholar 

  47. 47.

    Hahsler, M., Hornik, K. & Buchta, C. Getting things in order: an introduction to the R package seriation. J. Stat. Softw. 25, 1–34 (2008).

    Article  Google Scholar 

  48. 48.

    Hodges, J. L. & Lehmann, E. L. Estimates of location based on rank tests. Ann. Math. Stat. 34, 598–611 (1963).

    Article  Google Scholar 

  49. 49.

    Lehmann, E. L. Nonparametric confidence intervals for a shift parameter. Ann. Math. Stat. 34, 1507–1512 (1963).

    Article  Google Scholar 

Download references

Acknowledgements

We thank Barcode for Life and Stichting Hetty Odink (no. R3545) for the financial support of the clinical studies and WGS analyses. This publication and the underlying study have been made possible partly by the data that the Hartwig Medical Foundation and CPCT have made available to the study. We would like to thank all local principal investigators and medical specialists, and the nurses of all contributing centers for their help with patient recruitment. We are particularly grateful to all participating patients and their families. This work was supported in parts by grants from the Pink Ribbon Foundation (no. 204-184) and CZ healthcare insurance (no. CZ-201300460). M.S. was supported by Cancer Genomics Netherlands through a grant from the Netherlands Organization of Scientific Research. H.V.D.W., J.V.R. and the Erasmus MC Cancer Computational Biology Center were financed through a grant from the Daniel den Hoed Foundation.

Author information

Affiliations

Authors

Contributions

L.A., M.S., S.M.W., J.W.M.M. and S.S. wrote the manuscript, which all authors reviewed. M.S., J.V.R. and H.J.G.V.D.W. performed the bioinformatics analyses. L.A. and S.S. managed the clinical data assessment. A.V.H., L.N. and E.C. provided the CHORD (HRD) prediction scores. T.G.S., V.C.G.T.H., M.L., J.M.G.H.V.R., H.J.B., N.S., A.J. and S.S. are the main clinical contributors. H.J.B., M.P.L., E.E.V. and S.S. are members of the CPCT-02 study team and/or CPCT board. S.N.Z. provided assistance that allowed the comparisons with the primary breast cancer cohort (BASIS cohort). E.C. coordinated the sequencing of the samples and contributed to the bioinformatics analyses.

Corresponding author

Correspondence to John W. M. Martens.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–10

Reporting Summary

Supplementary Table 1

Patient characteristics.

Supplementary Table 2

Cohorts for the comparison of genomic alterations.

Supplementary Table 3

Frequency of affected driver genes (defined by dN/dScv) per breast cancer subtype.

Supplementary Table 4

Frequency of driver genes (93 breast cancer driver genes reported by Nik-Zainal et al.) per breast cancer subtype.

Supplementary Table 5

Gains and losses defined by GISTIC2.0 (v.2.0.23).

Supplementary Table 6

Actionable alterations according to OncoKB (12 July 2018).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Angus, L., Smid, M., Wilting, S.M. et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet 51, 1450–1458 (2019). https://doi.org/10.1038/s41588-019-0507-7

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing