The clonal and mutational evolution spectrum of primary triple-negative breast cancers

Journal name:
Nature
Volume:
486,
Pages:
395–399
Date published:
DOI:
doi:10.1038/nature10933
Received
Accepted
Published online

Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers1. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time—to our knowledge—in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC2, 3 showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.

At a glance

Figures

  1. Distribution of number of validated somatic mutations by case over 65 cases.
    Figure 1: Distribution of number of validated somatic mutations by case over 65 cases.

    a, Mutation frequency (basal, red; other, grey). Patients harbouring known driver gene mutations are indicated. b, Case-specific and overall (inset) distributions of mutations in CNA classes. AMP, amplification; GAIN, single copy gain; HETD, hemizygous deletion; HLAMP, high-level amplification; HOMD, homozygous deletion; NEUT, no copy number change. The number of (HOMD, HLAMP) CNAs (black diamonds) and percentage genome altered (green circles) are indicated.

  2. Population patterns of co-occurrence and mutual exclusion of genomic aberrations in TNBC.
    Figure 2: Population patterns of co-occurrence and mutual exclusion of genomic aberrations in TNBC.

    a, Case-specific mutations in known driver genes, plus genes from integrin signalling and ECM-related proteins (laminins, collagens, integrins, myosins and dynein) derived from all aberration types: high-level amplifications (HLAMP), homozygous deletions (HOMD), missense, truncating, splice site and indel somatic mutations are depicted in genes with at least two aberrations in the population. b, Distribution of somatic mutations in 25 genes across all exons of 159 additional breast cancers (relative proportion of ER+ cases in green, and ER in blue), shown as a percentage of cases (in parentheses) with one or more mutations. *P<0.05.

  3. Network analysis of 254 recurrently mutated genes by somatic point mutations and indels.
    Figure 3: Network analysis of 254 recurrently mutated genes by somatic point mutations and indels.

    a, Case-specific mutations shaded according to clonal frequencies in known driver genes, plus genes from integrin signalling and ECM-related proteins (laminins, collagens, integrins, myosins and dyneins). b, Significantly overrepresented pathways (FDR<0.001) from recurrently mutated genes (see Supplementary Methods). Node shading encodes the adjusted P value (q value) of the comparison of the distribution of clonal frequencies of mutations in a given pathway to the overall distribution of clonal frequencies. A spectrum of higher (red) and lower (yellow) clonal frequencies is evident. Letters in parentheses indicate database sources.

  4. Clonal evolution in TNBC.
    Figure 4: Clonal evolution in TNBC.

    a, Schematic representation of integration of CNA, LOH, allelic abundance measurements and normal cell contamination for clonal frequency estimation using a Dirichlet process (DP) model (left). Example of a mixture of three clonal genotypes composed of four mutations (A, B, C, D) and their resulting clonal frequencies. b, Estimated clonal frequencies for four cases are shown as the distribution of posterior probabilities from the pyclone model (Supplementary Methods). Clonal frequency distributions are coloured by their frequency group membership. c, Left, relationship of mutation abundance (synonymous (Syn) and non-synonymous (Non-syn)) and the inferred number of clonal clusters. Middle, distribution and kernel density (red line) of the number of inferred clonal clusters over 54 TNBCs. Right, kernel density distribution of clonal clusters for basal (red) and non-basal (grey) tumours.

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Sequence Read Archive

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Author information

  1. These authors contributed equally to this work.

    • Andrew Roth,
    • Rodrigo Goya,
    • Arusha Oloumi,
    • Gavin Ha,
    • Yongjun Zhao,
    • Gulisa Turashvili,
    • Jiarui Ding,
    • Kane Tse,
    • Gholamreza Haffari &
    • Ali Bashashati

Affiliations

  1. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada

    • Sohrab P. Shah,
    • Andrew Roth,
    • Arusha Oloumi,
    • Gavin Ha,
    • Gulisa Turashvili,
    • Jiarui Ding,
    • Gholamreza Haffari,
    • Ali Bashashati,
    • Leah M. Prentice,
    • Jaswinder Khattra,
    • Angela Burleigh,
    • Damian Yap,
    • Andrew McPherson,
    • Karey Shumansky,
    • Anamaria Crisan,
    • Ryan Giuliany,
    • Alireza Heravi-Moussavi,
    • Jamie Rosner,
    • Daniel Lai,
    • Peter Watson,
    • David Huntsman &
    • Samuel Aparicio
  2. Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia V5Z 1L3, Canada

    • Sohrab P. Shah,
    • Andrew Roth,
    • Arusha Oloumi,
    • Gavin Ha,
    • Gulisa Turashvili,
    • Jiarui Ding,
    • Gholamreza Haffari,
    • Ali Bashashati,
    • Leah M. Prentice,
    • Jaswinder Khattra,
    • Angela Burleigh,
    • Damian Yap,
    • Andrew McPherson,
    • Karey Shumansky,
    • Anamaria Crisan,
    • Ryan Giuliany,
    • Alireza Heravi-Moussavi,
    • Jamie Rosner,
    • Daniel Lai,
    • David Huntsman &
    • Samuel Aparicio
  3. Canada’s Michael Smith Genome Sciences Centre, Vancouver, British Columbia V5Z 1L3, Canada

    • Rodrigo Goya,
    • Yongjun Zhao,
    • Kane Tse,
    • Inanc Birol,
    • Richard Varhol,
    • Angela Tam,
    • Noreen Dhalla,
    • Thomas Zeng,
    • Kevin Ma,
    • Simon K. Chan,
    • Malachi Griffith,
    • Annie Moradian,
    • S.-W. Grace Cheng,
    • Gregg B. Morin,
    • Steven Jones,
    • Martin Hirst &
    • Marco A. Marra
  4. Centre for Molecular Medicine and Therapeutics, 950 West 28th Avenue, Vancouver, British Columbia V5Z 4H4, Canada

    • Virginie Bernard &
    • Wyeth W. Wasserman
  5. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada

    • Gregg B. Morin,
    • Irmtraud M. Meyer,
    • Wyeth W. Wasserman,
    • Steven Jones &
    • Marco A. Marra
  6. British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, British Columbia V5Z 4E6, Canada

    • Peter Watson,
    • Karen Gelmon &
    • Stephen Chia
  7. Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

    • Suet-Feung Chin,
    • Christina Curtis,
    • Oscar M. Rueda,
    • Paul D. Pharoah &
    • Carlos Caldas
  8. Department of Oncology, University of Cambridge, Hills Road, Cambridge CB2 2XZ, UK

    • Suet-Feung Chin,
    • Christina Curtis,
    • Oscar M. Rueda &
    • Carlos Caldas
  9. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA

    • Christina Curtis
  10. Departments of Oncology and Laboratory Medicine and Pathology, University of Alberta, 11560 University Avenue, Cross Cancer Institute, Edmonton, Alberta T6G 1Z2, Canada

    • Sambasivarao Damaraju &
    • John Mackey
  11. Life Technologies, 101 Lincoln Centre Dr., Foster City, California 94404, USA

    • Kelly Hoon,
    • Timothy Harkins &
    • Vasisht Tadigotla
  12. Department of Pathology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California 94143, USA

    • Mahvash Sigaroudinia,
    • Philippe Gascard &
    • Thea Tlsty
  13. Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94143, USA

    • Joseph F. Costello
  14. Department of Computer Science, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

    • Irmtraud M. Meyer
  15. Centre for High-Throughput Biology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

    • Irmtraud M. Meyer &
    • Martin Hirst
  16. Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada

    • Connie J. Eaves
  17. Department of Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Dr., Burnaby, British Columbia V5A1S6, Canada

    • Steven Jones
  18. Centre for Translational and Applied Genomics, BC Cancer Agency, 600 West 10th Ave, Vancouver, British Columbia V5Z 4E6, Canada

    • David Huntsman
  19. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada

    • Martin Hirst
  20. Cambridge Breast Unit, Addenbrookes Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK

    • Carlos Caldas
  21. Cambridge Experimental Cancer Medicine Centre (ECMC), Cambridge CB2 0RE, UK

    • Carlos Caldas

Contributions

S.A., S.P.S., C. Caldas and M.A.M. designed and implemented the research plan and wrote the manuscript. S.P.S., A.R., R. Goya, G. Ha, J.D., G. Haffari, A. Bashashati, A. McPherson, K.S., A.C., R. Giuliany, A.H.-M., J.R., D.L., I.B., R.V., S.W.C., M.G., I.M.M., S.J., C. Curtis, O.M.R., P.D.P., V.B. and W.W.W. conducted bioinformatic analyses of the data and/or gave advice on analytic methodology. G.T. conducted histopathological review and immunohistohemistry. A.O., Y.Z., G.T., K.T., L.M.P., J.K., A.B., D.Y., A.T., N.D., T.Z., S.-F.C., K.M. and M.H. conducted sequencing or experimental validation of somatic aberrations. D.Y., A. Moradian, S.-W.G.C. and G.B.M. conducted proteome validation of splicing. P.W., K.G., S.C., S.-F.C., G.T., J.M., C. Caldas, P.D.P. and D.H. collected and interpreted clinical data. S.D., J.F.C., T.T., M.S., P.G. and C.J.E. contributed materials or reagents. K.H., V.T., T.H., M.H. and M.A.M. generated sequence data.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Aligned exome/genome sequence data, RNA-seq data and Affymetrix SNP6.0 data sets are available at the European Genome-phenome Archive (http://www.ebi.ac.uk/ega/) under study accession number EGAS00001000132. Normal reference RNA-seq datasets are available at the NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/Traces) under study accession number SRP000930.

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Supplementary information

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  1. Supplementary Information (22M)

    This file contains legends for Supplementary Figures 1-12 (see pages 2-5), Supplementary Figures 1-12 (see pages 6-127), Supplementary Methods with additional references (see pages 128-140) and legends for Supplementary Tables 1-17 with additional references (see pages 142-145).

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  1. Supplementary Data (4.9M)

    This file contains Supplementary Tables 1-17.

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