Single‐Cell Transcriptomic Identification of Drug‐Tolerant Persister Mechanisms in Capecitabine‐Treated Triple‐Negative Breast Cancer

Vollständiger Abstract

Worum geht es in dieser Arbeit?

ABSTRACT Triple‐negative breast cancer (TNBC) is an aggressive subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. This lack of receptors limits targeted therapies and contributes to high rates of recurrence. A subset of tumor cells survives even intensive chemotherapy, and growing evidence suggests that drug‐tolerant persister cells (DTPs) play a central role in this survival, evading treatment and later driving relapse. However, the molecular programs that support their survival across different treatment conditions remain poorly understood. To address this gap, this study investigates the transcriptional landscape of TNBC at single‐cell resolution to characterize transcriptional programs associated with capecitabine‐induced drug tolerance relative to the chemonaive state. Single‐cell RNA sequencing (scRNA‐seq) data from capecitabine‐treated and chemonaive patient‐derived xenograft (PDX) models were processed using a Seurat‐based pipeline, including quality control, SCTransform normalization, batch correction, and data integration. Cell clustering and annotation were performed using SingleR with the Human Primary Cell Atlas reference. Differential expression analysis using the MAST model compared epithelial cells across conditions, followed by protein–protein interaction (PPI) network construction and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. After quality filtering, 42,015 high‐quality cells were retained from 57,943 cells. These cells formed 16 clusters representing 36 distinct cell types, with platelets and neutrophils comprising the largest populations and epithelial cells representing a smaller but biologically critical malignant compartment (4.2%). We identified 481 upregulated genes in treated epithelial cells, including NECTIN4, ACTN4, RHOV , and RPN2 . PPI network analysis further identified highly connected hub genes, including RPL18A, RPL19, RPL35, RPL8, RPS11, RPS3 , and UBA52 , predominantly associated with translational and ribosomal pathways. Given the known tendency of network centrality methods to prioritize housekeeping genes, these findings are presented as candidate associations warranting further mechanistic investigation. Together, these findings define a transcriptional program associated with capecitabine‐induced drug tolerance in TNBC. They also highlight candidate molecular targets for overcoming therapy resistance and reducing disease recurrence, pending functional and independent validation.