Multiplex-GCN predicts clinically actionable tumor microenvironment subtypes in pancreatic cancer

[bioRxiv] A bioRxiv preprint by Bose et al. describes a multi-layered graph convolutional network (GCN) developed to identify tumor microenvironment (TME)-guided subtypes of pancreatic ductal adenocarcinoma (PDAC). The authors first built a TME reference atlas from single-cell RNA-seq data covering immune and non-immune cell types, then used in silico cytometry to estimate cell-type proportions in bulk tumor RNA-seq samples from Cancer Genome Atlas PDAC patients. They integrated patient-specific cellular proportions with multi-omics data, including gene expression, DNA methylation, copy number variation and clinical data, in the GCN model, and predicted PDAC subtypes by unsupervised clustering of the GCN-based embedding matrices. According to the authors, the method identified seven PDAC subtypes, which they grouped into two ecological tumor families; the subtypes showed distinct immune infiltration, stromal activity and malignant epithelial cell abundance, differential expression analysis revealed subtype-specific molecular programs, and survival analysis showed significant differences in overall survival across ecological families and subtypes. The authors said the approach provides a biologically informed framework for patient stratification with potential implications for precision oncology; the findings are preprint results and have not been peer reviewed.