Deciphering the Heterogeneity of Pancreatic Cancer: DNA Methylation-Based Cell Type Deconvolution Unveils Distinct Subgroups and Immune Landscapes

Abstract

<b>Background:</b> Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy, characterized by low tumor cellularity, a dense stromal response, and intricate cellular and molecular interactions within the tumor microenvironment (TME). Although bulk omics technologies have enhanced our understanding of the molecular landscape of PDAC, the specific contributions of non-malignant immune and stromal components to tumor progression and therapeutic response remain poorly understood. <b>Methods:</b> We explored genome-wide DNA methylation and transcriptomic data from the Cancer Genome Atlas Pancreatic Adenocarcinoma cohort (TCGA-PAAD) to profile the immune composition of the TME and uncover gene co-expression networks. Bioinformatic analyses included DNA methylation profiling followed by hierarchical deconvolution, epigenetic age estimation, and a weighted gene co-expression network analysis (WGCNA). <b>Results:</b> The unsupervised clustering of methylation profiles identified two major tumor groups, with Group 2 (n = 98) exhibiting higher tumor purity and a greater frequency of <i>KRAS</i> mutations compared to Group 1 (n = 87) (<i>p</i> &lt; 0.0001). The hierarchical deconvolution of DNA methylation data revealed three distinct TME subtypes, termed hypo-inflamed (immune-deserted), myeloid-enriched, and lymphoid-enriched (notably T-cell predominant). These immune clusters were further supported by co-expression modules identified via WGCNA, which were enriched in immune regulatory and signaling pathways. <b>Conclusions:</b> This integrative epigenomic-transcriptomic analysis offers a robust framework for stratifying PDAC patients based on the tumor immune microenvironment (TIME), providing valuable insights for biomarker discovery and the development of precision immunotherapies.