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

Abstract

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.

Tumor mutational burden is a determinant of immune-mediated survival in breast cancer

“…To test the reproducibility of this observation in an independent cohort, we utilized the METABRIC study consisting of > 2,000 breast tumors. 24 While the TCGA and METABRIC platforms for expression profiling and mutation analysis differed, the derivation of immune subclasses and TMB were determined to be moderately comparable between the two data sets (see Methods and eFigures 4–5 in Supplement 1). For computing TMB in the METABRIC dataset, the number of mutations/tumor (i.e., mutation count), but not the mutation rate, was available.…”

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

“…Five different datasets were included for several major cancer types, in particular breast carcinoma (TCGA consortium), 31–34 colorectal carcinoma (http://www.intgen.org or TCGA consortium), 35,36 non-small cell lung carcinoma (TCGA consortium) 37–40 and melanoma, 41–45 to study the correlation between the expression level of metagenes indicating the presence of immune cell types and a variety of chemotactic factors and receptors. An extra dataset concerning breast carcinoma 46 was used for studying expression variability and treatment response.…”

A patient‐derived explant (PDE) model of hormone‐dependent cancer

“…Breast cancer is highly heterogeneous, clustering into 10 different molecular subgroups based on an integrated analysis of genomic aberrations and transcriptional profiling (Curtis et al ., 2012). Tumors that express ERα represent the majority (≥ 70%) of all cases (Curtis et al ., 2012).…”

“…Tumors that express ERα represent the majority (≥ 70%) of all cases (Curtis et al ., 2012). Assessment of ERα and PGR status by immunohistochemistry guides treatment decisions for breast cancer, as PGR is an ERα‐regulated gene and used as a biomarker of ERα activation (Lee and Gorski, 1996).…”