A new genome-driven integrated classification of breast cancer and its implications

Abstract

Breast cancer is a group of heterogeneous diseases that show substantial variation in their molecular and clinical characteristics. This heterogeneity poses significant challenges not only in breast cancer management, but also in studying the biology of the disease. Recently, rapid progress has been made in understanding the genomic diversity of breast cancer. These advances led to the characterisation of a new genome-driven integrated classification of breast cancer, which substantially refines the existing classification systems currently used. The novel classification integrates molecular information on the genomic and transcriptomic landscapes of breast cancer to define 10 integrative clusters, each associated with distinct clinical outcomes and providing new insights into the underlying biology and potential molecular drivers. These findings have profound implications both for the individualisation of treatment approaches, bringing us a step closer to the realisation of personalised cancer management in breast cancer, but also provide a new framework for studying the underlying biology of each novel subtype.

Figure 1

The genomic and transcriptomic landscape of the 10 integrative clusters. Genome-wide frequencies (Freq) of somatic copy number alterations (Y axis, upper plot) and the subtype-specific association (−log₁₀ P-value) of gene expression (Y axis, bottom plot) based on the differential expression in each of the 10 integrative clusters compared to the rest. Regions of copy number gain are indicated in red and regions of loss in blue in the frequency plot (upper plot). Overexpressed genes are represented as positive and underexpressed genes as negative (lower plot). The left bars show the composition of each cluster in terms of the intrinsic subtypes (red: basal, pink: HER2, dark blue: luminal A, light blue: luminal B and green: normal-like).

Figure 2

Relationship between the 10 integrative clusters and ER expression, HER2 expression and PR expression. IHC, immunohistochemistry; Expression, mRNA gene expression; SNP6=copy number alteration as assessed by Affymetrix SNP 6.0 array.

Figure 3

The 10 integrative clusters represent distinct entities. (A) Distinct clinical features across the 10 integrated clusters. In the left plot, the coloured diamonds mark clinical features demonstrating significant subtype-specific associations. NPI, Nottingham prognostic index. The intensity of the colours in the right plot represents the frequency of each variable in every cluster. (B) Kaplan–Meier curves of disease-specific survival (left) and overall survival (right) across the 10 integrated clusters (truncated at 15 years). For each cluster, the number of cases at risk is indicated as well as the total number of deaths (in parentheses).

Figure 4

The genomic instability and mutational landscape of the 10 breast cancer subtypes. (A) Genomic instability across the 10 integrative clusters. Genomic instability was measured by the area under the segmented means of the log ratios. (B) Somatic mutation spectrum across the 10 integrative clusters. The Cancer Genome Atlas Network has characterised the mutational landscape of ∼500 breast cancers, also classified into the 10 integrative clusters, using exome sequencing (TCGA, 2012). The top 10 genes mutated in each integrative cluster are represented. Mutational frequency is displayed on the Y axis. Red asterisks indicate genes where the mutational frequency shows a significant subtype-specific association.