Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Abstract

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms the single-cell gene expression matrix into a pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to pancreatic ductal adenocarcinoma (PDAC) scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggested a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.