Unravelling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis

Abstract

Background: Recent efforts of genome-wide gene expression profiling analyses have improved our understanding of the biological complexity and diversity of triple-negative breast cancers (TNBCs) reporting, at least six different molecular subtypes of TNBC namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). However, little is known regarding the potential driving molecular events within each subtype, their difference in survival and response to therapy. Further insight into the underlying genomic alterations is therefore needed.

Patients and methods: This study was carried out using copy-number aberrations, somatic mutations and gene expression data derived from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas. TNBC samples (n = 550) were classified according to Lehmann's molecular subtypes using the TNBCtype online subtyping tool (http://cbc.mc.vanderbilt.edu/tnbc/).

Results: Each subtype showed significant clinic-pathological characteristic differences. Using a multivariate model, IM subtype showed to be associated with a better prognosis (HR = 0.68; CI = 0.46-0.99; P = 0.043) whereas LAR subtype was associated with a worst prognosis (HR = 1.47; CI = 1.0-2.14; P = 0.046). BL1 subtype was found to be most genomically instable subtype with high TP53 mutation (92%) and copy-number deletion in genes involved in DNA repair mechanism (BRCA2, MDM2, PTEN, RB1 and TP53). LAR tumours were associated with higher mutational burden with significantly enriched mutations in PI3KCA (55%), AKT1 (13%) and CDH1 (13%) genes. M and MSL subtypes were associated with higher signature score for angiogenesis. Finally, IM showed high expression levels of immune signatures and check-point inhibitor genes such as PD1, PDL1 and CTLA4.

Conclusion: Our findings highlight for the first time the substantial genomic heterogeneity that characterize TNBC molecular subtypes, allowing for a better understanding of the disease biology as well as the identification of several candidate targets paving novel approaches for the development of anticancer therapeutics for TNBC.

Figure 1.

Mutational landscape of TNBC molecular subtypes. Frequencies of mutations across each TNBC molecular subtype according to the different types of mutations. Only genes mutated at a frequency >10% in at least one subtype are displayed. Significant differences (FDR < 0.05) are shown in red (right panel).

Figure 2.

Genomic instability within each TNBC molecular subtype. CNA frequencies for the 32 breast cancer copy number driver genes across each TNBC molecular subtype. Significant differences (FDR < 5%, one-sided Fisher test) are shown with an asterisk.

Figure 3.

Altered signalling pathways and deregulated hallmarks of cancer signatures within each TNBC molecular subtype. (A) Genomic and transcriptomic alterations profiles involving PDGF/VEGF and PI(3)K/RTK/RAS signalling pathway. Copy-number frequency is reported for each TNBC molecular subtype inside each box. Copy number gain frequency is presented in red while copy number losses are in blue. Differences in mRNA expression were tested using one-sided Wilcoxon Rank Sum test with P-values corrected for multi-testing. Significant mRNA upregulation and downregulation were displayed using, respectively, red and blue triangles. Somatic mutation frequency is reported, when available, on top of each TNBC molecular subtype. (B) Heatmap of the 10 Hallmarks of cancer meta-gene signature scores for the TNBC molecular subtypes. Differences were reported using one-sided Wilcoxon Rank Sum test with P-values corrected for multi-testing. Significant differences were reported for FDR lower than 5% (significant up-regulation are displayed in black and down-regulated in white).