The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

By C. Curtis, Sohrab P. Shah, S. Chin, G. Turashvili, O. Rueda, M. Dunning, Doug Speed, A. Lynch, S. Samarajiwa, Yinyin Yuan and 21 more. Nature, 2012

5,719 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd8a6c21b0db46a7b7e47e9a51f2b3ede2a0d20b

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Citations in the corpus

Pattern discovery and cancer gene identification in integrated cancer genomic data (2013, methodology): “The method was recently used in a landmark study to predict novel breast cancer subtypes with distinct clinical outcomes (9), and it was found that the joint clustering of copy number and gene expression pro ﬁ les resolved the considerable heterogeneity of the expression-only subgroups.”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, background): “Large curated ‘ground-truth’ datasets of matched genomic, histopathological and clinical outcome data are scarce beyond the publicly available datasets, such as The Cancer Genome Atlas (TCGA) [34], International Cancer Genome Consortium (ICGC) [35], Gene Expression Omnibus (GEO) [36], European Genom”
Breast cancer intrinsic subtype classification, clinical use and future trends. (2015, background): “, lacking copy number and cis-acting alterations [88].”
Artificial intelligence and machine learning in precision and genomic medicine (2021, background): “Similarly, combining the copy number of DNA variations with breast cancer patients' gene expression helped researchers who learn the disease's mechanism and developed new treatment strategies [119].”
Artificial intelligence in oncology (2020, positive): “61 This scoring system stratified prognosis more accurately than existing methods in several independent test data-sets.”