The shaping and functional consequences of the microRNA landscape in breast cancer

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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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Figure 1: Factors shaping the miRNA landscape across the breast cancer genome.
Figure 2: Cellular processes connected to the miRNA landscape.
Figure 3: miRNAs have an increased prognostic value in the genomically stable iClust4.
Figure 4: miRNAs have a significant modulatory role in iClust4.

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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.

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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.

Corresponding authors

Correspondence to Anna Git or Sam Aparicio or Carlos Caldas.

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The authors declare no competing financial interests.

Supplementary information

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. (PDF 8229 kb)

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. (ZIP 3021 kb)

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Dvinge, H., Git, A., Gräf, S. et al. The shaping and functional consequences of the microRNA landscape in breast cancer. Nature 497, 378–382 (2013). https://doi.org/10.1038/nature12108

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