Analysis of heterogeneity in subpopulations of breast cancer using single-cell RNA sequencing

Abstract

Abstract Breast cancer is a complex disease with high levels of intra-tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) can identify the gene expression profile of different cell subpopulations, revealing key subpopulations that drive tumor progression and therapeutic resistance. We analyzed single-cell RNA-seq data from 26 primary tumors from three major clinical breast cancer subtypes. We inferred copy number variation (CNV) and identified 8 clusters of malignant epithelial cells, with Cluster 1 being the most widely present in breast cancer. We identified 28 subpopulations based on gene-expression profiles, with five subpopulations shared by multiple patients. We identified specific ligand-receptor interactions between different subpopulations and identified key pathway-associated prognostic markers, including EPHA3, JAML, LCK, and SEMA3B, which could serve as potential biomarkers. The tumor microenvironment plays a significant role in tumor growth and metastasis. Targeting proteins involved in the interaction between cancer cells and the microenvironment, including ALCAM, CD6, and Adgre5, has shown promise in preclinical cancer models. This study could provide valuable information to identify biomarkers for individualized cancer therapy, which is challenging due to high levels of intra-tumor heterogeneity.