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

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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.

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).

  2. 2.

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

  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).

  4. 4.

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

  5. 5.

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

  6. 6.

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

  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).

  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).

  9. 9.

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

  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).

  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).

  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).

  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).

  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).

  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).

  16. 16.

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

  17. 17.

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

  18. 18.

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

  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).

  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).

  21. 21.

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

  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).

  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).

  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).

  25. 25.

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

  26. 26.

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

  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).

  29. 29.

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

  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).

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

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

  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).

  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).

  38. 38.

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

  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).

  40. 40.

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

  41. 41.

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

  42. 42.

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

  43. 43.

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

  44. 44.

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

  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).

  46. 46.

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

  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).

  48. 48.

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

  49. 49.

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

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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.

Author information

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.

Correspondence to John W. M. Martens.

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The authors declare no competing interests.

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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) doi:10.1038/s41588-019-0507-7

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