Computational epigenomic analysis reveals co-occurring DNA methylation patterns in triple-negative breast cancer, highlighting distinct tumor biology and immune microenvironment features.
Abstract DNA methylation deregulation is an essential feature of tumor biology, influencing cancer formation and pathogenesis. Analyses of DNA methylation in bulk cancer specimens are challenging due to the high data dimensionality, noise, and mixture of different cell types. Here we characterized methylation dynamics in cancer by investigating the variance structure of bulk tumor DNA methylation data through identification of groups of highly correlated CpGs, termed CpG cassettes. Using triple-negative breast cancer (TNBC) as a model system our approach identified co-occurring methylation patterns linked to different intrinsic tumor processes and pathways, gene inactivation, and composition of the tumor immune microenvironment. Our framework also demonstrated the presence of high variance DNA methylation seemingly not linked to tumor biology that could be excluded using chromatin accessibility filtering. Together, this work outlines a comprehensive approach to analyze bulk tumor DNA methylation data combining tumor purity adjustment, functional CpG filtering, stratification by CpG contexts, and identification of highly correlated CpG modules to enhance tumor-intrinsic DNA methylation patterns and our understanding of processes shaping epigenetic tumor evolution.
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