Comprehensive molecular portraits of human breast tumours

Abstract

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.

Figures

Figure 1. Significantly Mutated Genes (SMG) and correlations with genomic and clinical features

Tumor samples are grouped by mRNA-subtype: Luminal A (n=225), Luminal B (n=126), HER2E (n=57), and Basal-like (n=93). Left: Non-silent somatic mutation patterns and frequencies for SMGs. Middle: Clinical features: black, positive or T2-4; white, negative or T1; grey, NA or equivocal. Right: SMGs with frequent copy number amplifications (red) or deletions (blue). Far Right: Non-silent mutation rate per tumor (mutations per megabase, adjusted for coverage). Average mutation rate for each expression subtype is indicated. Hypermutated: mutation rates > 3 SD above the mean (> 4.688, indicated by grey line).

Figure 2. Coordinated analysis of breast cancer subtypes defined from five different genomic/proteomic platforms

a) Consensus clustering analysis of the subtypes identifies four major groups (samples, n=348). The blue and white heatmap displays sample consensus. b) Heatmap display of the subtypes defined independently by microRNAs, DNA methylation, copy number, PAM50 mRNA expression, and RPPA expression. Red bar indicates membership of a cluster type. c) Associations with molecular and clinical features. P-values were calculated using a Chi-square test.

Figure 3. Integrated analysis of the PI3K, TP53, and RB1 pathways

Breast cancer subtypes differ by genetic and genomic targeting events, with corresponding effects on pathway activity. For a) PI3K, b) TP53 and c) RB1 pathways, key genes were selected using prior biological knowledge. Multiple mRNA expression signatures for a given pathway were defined (details in Supplemental Methods; PI3K:Saal, PTEN loss in human breast tumors; PI3K:CMap, PI3K/mTOR inhibitor treatment in vitro; PI3K:Majumder, Akt over-expression in mouse model; p53:IARC, expert-curated p53 targets; p53:GSK, TP53 mutant versus wild-type cell lines; p53:KANNAN, p53 over-expression in vitro; p53:TROESTER, TP53 knockdown in vitro; Rb:CHICAS, RB1 mouse knockout versus wild-type; Rb:LARA, RB1 knockdown in vitro; Rb:HERSCHKOWITZ, RB1 LOH in human breast tumors) and applied to the gene expression data, in order to score each tumor for relative signature activity (yellow: more active). The PI3K panel includes a protein-based (RPPA) proteomic signature. Tumors were ordered first by mRNA-subtype, though specific ordering differs between the panels. P-values were calculated by a Pearson’s correlation or a Chi-squared test.

Figure 4. Mutual Exclusivity Modules in Cancer (MEMo) analysis

Mutual exclusivity modules are represented by their gene components and connected to reflect their activity in distinct pathways. For each gene, the frequency of alteration in Basal-like (right box) and non-Basal (left box) is reported. Next to each module is a fingerprint indicating what specific alteration is observed for each gene (row) in each sample (column). a) MEMo identified several overlapping modules that recapitulate the RTK/PI3K and p38/JNK1 signaling pathways and whose core was the top-scoring module. b) MEMo identified alterations to TP53 signaling as occurring within a statistically significant mutually exclusive trend. c) A Basal-like only MEMo analysis identified one module that included ATM mutations, defects at BRCA1 and BRCA2, and deregulation of the RB1-pathway. A gene expression heatmap is below the fingerprint to show expression levels.

Figure 5. Comparison of Breast and Serous Ovarian carcinomas

a) Significantly enriched genomic alterations identified by comparing Basal-like or Serous Ovarian tumors to Luminal cancers. b) Inter-sample correlations (yellow: positive) between gene transcription profiles of breast tumors (columns; TCGA data, arranged by subtype) and profiles of cancers from various tissues of origin (rows; external “TGEN expO” dataset, GSE2109) including Ovarian cancers.