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

Nature Genetics volume 51, pages 1450–1458 (2019)Cite this article

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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.

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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.

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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.

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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.

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Authors and Affiliations

  1. Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands

    Lindsay Angus, Marcel Smid, Saskia M. Wilting, Job van Riet, Martijn P. Lolkema, Agnes Jager, Stefan Sleijfer & John W. M. Martens

  2. Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands

    Job van Riet & Harmen J. G. van de Werken

  3. Department of Urology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands

    Job van Riet & Harmen J. G. van de Werken

  4. Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands

    Arne Van Hoeck, Luan Nguyen & Edwin Cuppen

  5. Department of Medical Genetics, The Clinical School, University of Cambridge, Cambridge, UK

    Serena Nik-Zainal

  6. Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands

    Tessa G. Steenbruggen, Neeltje Steeghs & Emile E. Voest

  7. Department of Medical Oncology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands

    Vivianne C. G. Tjan-Heijnen

  8. Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Mariette Labots

  9. Department of Internal Medicine, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands

    Johanna M. G. H. van Riel

  10. Department of Medical Oncology, Meander Medical Center, Amersfoort, The Netherlands

    Haiko J. Bloemendal

  11. Center for Personalized Cancer Treatment, Rotterdam, The Netherlands

    Haiko J. Bloemendal, Neeltje Steeghs, Martijn P. Lolkema, Emile E. Voest, Stefan Sleijfer & John W. M. Martens

  12. Hartwig Medical Foundation, Amsterdam, The Netherlands

    Edwin Cuppen

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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.

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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).

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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

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