The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Open accessAbstract
The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
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Numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis.
“Currently, numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis [109,110,111].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [111]
iCluster was employed in a study using genomic and transcriptomic data from 2,000 breast tumours that discovered novel subgroups.
“This framework was also employed for a landmark study which used genomic and transcriptomic data from 2,000 breast tumours and discovered novel subgroups amongst them (Curtis et al., 2012).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Curtis et al., 2012
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- Authors
- METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., Yuan, Y., Gräf, S., Ha, G., Haffari, G., Bashashati, A., Russell, R., McKinney, S., Langerød, A., Green, A., Provenzano, E., Wishart, G., Pinder, S., Watson, P., Markowetz, F., Murphy, L., Ellis, I., Purushotham, A., Børresen-Dale, A.-L., Brenton, J. D., Tavaré, S., Caldas, C., & Aparicio, S.
- Affiliations
- University of Cambridge (GB)
- University of British Columbia (CA)
- Oslo University Hospital (NO)
- University of Nottingham (GB)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- King's College London (GB)
- University of Southern California (US)
- Corresponding author
- Caldas, Carlos
- Aparicio, Samuel
- ORCID
- Curtis, Christina
- Shah, Sohrab P.
- Chin, Suet-Feung
- Turashvili, Gulisa
- Rueda, Oscar M.
- Dunning, Mark J.
- Speed, Doug
- Lynch, Andy G.
- Samarajiwa, Shamith
- Yuan, Yinyin
- Gräf, Stefan
- Ha, Gavin
- Haffari, Gholamreza
- Bashashati, Ali
- Green, Andrew
- Provenzano, Elena
- Wishart, Gordon
- Pinder, Sarah
- Watson, Peter
- Markowetz, Florian
- Murphy, Leigh
- Ellis, Ian
- Purushotham, Arnie
- Brenton, James D.
- Tavaré, Simon
- Caldas, Carlos
- Aparicio, Samuel
- Venue
- Nature
- Published
- 2012-04-18
- Volume
- 486
- Issue
- 7403
- Pages
- 346-352
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3440846
- OpenAlex
- W2005895632
- Cited by
- 6,097
- References
- 32
- Citations by year
- 2026: 341 · 2025: 411 · 2024: 458 · 2023: 497 · 2022: 496 · 2021: 615 · 2020: 533 · 2019: 449 · 2018: 426 · 2017: 361 · 2016: 373 · 2015: 378 · 2014: 339 · 2013: 291 · 2012: 119
- FWCI
- 119.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- University of Southern California
- Michael Smith Health Research BC
- Cancer Research UK (A7199, 15601, 19556)
- National Institute for Health and Care Research (ACF-2012-14-006, NF-SI-0611-10154)
- University of Cambridge
- King's College London
- National Institutes of Health
- BC Cancer Agency
- NIHR Cambridge Biomedical Research Centre
- National Human Genome Research Institute (P50-HG02790, P50HG002790)
- Division of Mathematical Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- breast cancer, copy number alterations, somatic copy number alterations, gene expression, single nucleotide polymorphisms, PPP2R2A, MTAP, MAP2K4, estrogen receptor-positive breast cancer, adaptive immune response, basal-like breast cancer, chromosome 5 deletion, molecular stratification, prognosis
- MeSH terms
- Breast Neoplasms, Female, Humans, Prognosis, Genome, Human, Gene Expression Regulation, Neoplastic, Treatment Outcome, Polymorphism, Single Nucleotide, Gene Expression Profiling, Genomics, MAP Kinase Kinase 4, Genes, Neoplasm, Kaplan-Meier Estimate, Gene Regulatory Networks, Protein Phosphatase 2, DNA Copy Number Variations
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