DNA methylation profile of inflammatory breast cancer and its impact on prognosis and outcome

Abstract

Background: Inflammatory breast cancer (IBC) is a rare disease characterized by rapid progression, early metastasis, and a high mortality rate.

Methods: Genome-wide DNA methylation analysis (EPIC BeadChip platform, Illumina) and somatic gene variants (105 cancer-related genes) were performed in 24 IBCs selected from a cohort of 140 cases.

Results: We identified 46,908 DMPs (differentially methylated positions) (66% hypomethylated); CpG islands were predominantly hypermethylated (39.9%). Unsupervised clustering analysis revealed three clusters of DMPs characterized by an enrichment of specific gene mutations and hormone receptor status. The comparison among DNA methylation findings and external datasets (TCGA-BRCA stages III-IV) resulted in 385 shared DMPs mapped in 333 genes (264 hypermethylated). 151 DMPs were associated with 110 genes previously detected as differentially expressed in IBC (GSE45581), and 68 DMPs were negatively correlated with gene expression. We also identified 4369 DMRs (differentially methylated regions) mapped on known genes (2392 hypomethylated). BCAT1, CXCL12, and TBX15 loci were selected and evaluated by bisulfite pyrosequencing in 31 IBC samples. BCAT1 and TBX15 had higher methylation levels in triple-negative compared to non-triple-negative, while CXCL12 had lower methylation levels in triple-negative than non-triple-negative IBC cases. TBX15 methylation level was associated with obesity.

Conclusions: Our findings revealed a heterogeneous DNA methylation profile with potentially functional DMPs and DMRs. The DNA methylation data provided valuable insights for prognostic stratification and therapy selection to improve patient outcomes.

Keywords: Biomarker; DNA methylation, epigenetic regulation; Differentially methylated sites; Driver mutations; Gene expression.

Fig. 1

Methylation profile of the IBC internal dataset. A Distribution of the differentially methylated CpG probes between tumor and normal tissues (NT). The proportion of CpGs in relation to its gene location (TSS200/TSS1500, body/exons boundaries, intergenic regions, 5′UTR/first exon and 3′UTR); and to the CpG islands context (island, open sea, shelf, and shore) was based on the Illumina EPIC annotation. B Unsupervised K-means clustering analysis based on 46,908 identified DMPs revealed four clusters: cluster 1 is composed of adjacent normal samples and three clusters with IBC samples. Rows indicate the CpG sites, while columns represent samples. Clinical features of each case are represented below the heatmap along with targeted next-generation sequencing data for specific genes. The estrogen receptor (ER), progesterone receptor (PR), HER2 status, and the mutational pattern of TP53, BRCA1, BRCA2, and homologous recombination genes are indicated below the heatmap. C The survival curves (Kaplan–Meier and log-rank test) showed no significant statistical differences among clusters. D Univariate Cox regression analysis based on clinical variables and overall survival of 24 IBC patients evaluated by DNA methylation profiling. The forest plot shows the hazard ratios (squares), and the horizontal bars represent the range between the lower and upper limits of the 95% confidence intervals (CI) in the log₂ scale. IBC patients positive for estrogen (ER), progesterone (PR), and or human epidermal growth factor receptor 2 (HER2) were taken as a reference against triple-negative tumors. In the analysis of the BMI variable, we excluded patients within the normal weight range due to the small sample size (N = 4); overweight patients were used as a reference. For the remaining variables, we considered the absence of the corresponding predictive factor as a reference for analysis (*p < 0.05). E A panel of 105 cancer-related genes was investigated in 28 IBC patients using t-NGS. Highlights include clinical, molecular, and vital status information. Genes are organized in descending order of alterations. The top bar plots illustrate the number of altered genes detected in each sample, and the percentages on the right indicate the number of samples with genetic alterations for a given gene among all analyzed samples. Genes without selected variants were excluded

Fig. 2

Differentially methylated probes (DMPs) comparison performed among the internal dataset, 27 k Van der Auwera et al. (2010), and TCGA-BRCA advanced tumors. A KEGG pathway analysis shows five shared enriched pathways among three datasets. B Venn diagram obtained from dataset comparisons (internal dataset, TCGA-BRCA, and Van der Auwera et al., 2010) shows 385 shared DMPs among the datasets comparison

Fig. 3

Loci-specific DMRs analyzed by bisulfite pyrosequencing of CpGs associated with selected genes (BCAT1, CXCL12, and TBX15) using the internal cohort and cross-validation with gene expression levels using external cohorts (advanced III-IV stages from TCGA-BRCA and IBC from GSE45581). The first column shows the comparison of the means of DNA methylation levels of each associated DMRs containing the interrogated CpG probes and the flanking CpG dinucleotides between normal and IBC samples. Tumor samples were also dichotomized in non-TNBC and TNBC, and the methylation levels were compared. The DNA methylation levels of distinct DMPs confirmed higher methylation levels of BCAT1 and TBX15 genes and lower levels of CXCL12 in IBC. The methylation levels of TBX15 gene were associated with the subtype TNBC and obesity (*p ≤ 0.05, **p ≤ 0.01; ***p ≤ 0.001, and ****p ≤ 0.0001). ns: not significant (p > 0.05)