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Abstract

MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles1,2,3,4,5,6. Here we report the miRNA expression profiles of 1,302 breast tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data7. This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach8 to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA–mRNA interactions rather than as on–off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.

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Data deposits

The raw non-coding RNA microarray data is available through the European Genome–Phenome Archive (http://www.ebi.ac.uk/ega/), which is hosted by the EBI, under accession number GAS00000000122.

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Acknowledgements

The study was funded by Cancer Research UK and the British Columbia Cancer Foundation. The authors also acknowledge the support of the University of Cambridge, Hutchinson Whampoa, the NIHR Cambridge Biomedical Research Centre, and the Cambridge Experimental Cancer Medicine Centre. We thank S. McGuire for help in sample management; S. Fulmer-Smentek and T. Hill for help with array design; L. Goldstein for initial processing of sequencing data; O. Rueda for statistical advice; and O. Rueda, J. Carroll and R. Ali for reading of the manuscript. We are very grateful to the patients who donated tissue and associated pseudo-anonymized clinical data.

Author information

Author notes

    • Heidi Dvinge
    •  & Anna Git

    These authors contributed equally to this work.

    • Heidi Dvinge
    •  & Stefan Gräf

    Present addresses: Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA (H.D.); Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK (S.G.).

Affiliations

  1. Cancer Research UK Cambridge Institute and Department of Oncology, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

    • Heidi Dvinge
    • , Anna Git
    • , Stefan Gräf
    • , Suet-Feung Chin
    •  & Carlos Caldas
  2. European Molecular Biology Laboratory–European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

    • Mali Salmon-Divon
  3. Department of Molecular Biology, Ariel University Center of Samaria, Ariel 40700, Israel

    • Mali Salmon-Divon
  4. Department of Preventive Medicine, University of Southern California, Los Angeles, California 90033, USA

    • Christina Curtis
    •  & Andrea Sottoriva
  5. Genome Sciences Centre, BC Cancer Agency, 675 West 10th Avenue, Vancouver V5Z 1L3, Canada

    • Yongjun Zhao
    •  & Martin Hirst
  6. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver V6T 2B5, Canada

    • Yongjun Zhao
    • , Martin Hirst
    • , Gulisa Turashvili
    •  & Sam Aparicio
  7. Wellcome Trust Cancer Research UK Gurdon Institute and Department of Biochemistry, University of Cambridge, The Henry Wellcome Building of Cancer and Developmental Biology, Cambridge CB2 1QN, UK

    • Javier Armisen
    •  & Eric A. Miska
  8. Cambridge Breast Unit, Addenbrooke’s Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK

    • Elena Provenzano
    •  & Carlos Caldas
  9. Molecular Oncology, British Columbia Cancer Research Centre, Vancouver V5Z 1L3, Canada

    • Gulisa Turashvili
    •  & Sam Aparicio
  10. Department of Histopathology, School of Molecular Medical Sciences, University of Nottingham, Nottingham NG5 1PB, UK

    • Andrew Green
    •  & Ian Ellis
  11. Cambridge Experimental Cancer Medicine Centre, Cambridge CB2 0RE, UK

    • Carlos Caldas

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Contributions

H.D. and A.Git led the analysis and drafted the manuscript with C.Caldas; A.Git, S.G., S.A. and C.Caldas designed and coordinated the study; A.Git carried out all microarray and quantitative reverse transcriptase PCR laboratory work. Sequencing data were provided by Y.Z., M.H., J.A., E.A.M. and S.A. and analysed by M.S.-D., who also analysed external epigenetic data; S.G. designed custom microarray probes and contributed to array pre-processing. C.Curtis and A.S. processed external CNA data. S.-F.C., E.P., A.Green, I.E., G.T., S.A. and C.Caldas coordinated collection and processing of clinical material and associated clinical and histopathological information. S.A. and C.Caldas are joint senior authors and project co-leaders.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Anna Git or Sam Aparicio or Carlos Caldas.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Methods, Supplementary Appendix containing R source code for the modulatory effect of miRNAs, Supplementary References and Supplementary Figures 1-8.

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  1. 1.

    Supplementary Data

    This file contains Supplementary Tables 1-9. Supplementary Table 1 contains detailed clinical and histopathological information for the 1,302 tumors; Supplementary Table 2 shows annotation, potential cross-hybridization and distribution of 853 detectable miRNAs; Supplementary Table 3 shows miRNAs regulated by CNAs across the entire cohort or in ER+/ER- sub-cohorts; Supplementary Table 4 contains a list of minimal common regions of CNAs (across the entire cohort or in ER+/ER- sub-cohorts) which contain miRNAs and mRNAs; Supplementary Table 4 contains a list of minimal common regions of CNAs (across the entire cohort or in ER+/ER- sub-cohorts) which contain only miRNAs; Supplementary Table 5 shows intensity and correlation of detectable sibling miRNAs; Supplementary Table 6 contains a list of differentially expressed miRNAs between ER+ and ER- sub-cohorts or between Pam50 subtypes; Supplementary Table 7 shows calculated pairwise generalized additive model values for variable mRNAs and miRNAs; Supplementary Table 8 contains a list of significantly enriched Gene Ontology Biological Process terms in mRNAs clustered by miRNA-mRNA GAM values and Supplementary Table 9 contains a summary of ER and Her2 statuses of the breast cancer cell lines used in this study.

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DOI

https://doi.org/10.1038/nature12108

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