Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Reveals a Tissue-Resident Macrophage-Related Signature for Predicting Immunotherapy Response in Breast Cancer Patients

Simple Summary Immune checkpoint therapy (ICT) has proven to be a promising therapeutic approach to breast cancer (BC), but most patients with BC do not respond to ICT and there are no validated predictive biomarkers. Therefore, it is urgently necessary to identify a valuable biomarker for predicting ICT outcomes of BC patients. In this study, we performed scRNA-seq analysis and identified five tissue-resident macrophages (RTM) clusters with a mixed phenotype of M1-M2 macrophages. An integrated analysis of multi-omics data showed RTM clusters were characteristic of an elevated inflammatory response and reactive oxygen species pathway, and positively correlated with T cell cytotoxicity and infiltration of CD8+ T cells and CD8+ T cells, which is indicative of sensitivity to ICT. Therefore, the RTM clusters may serve as a valuable tool for clinical decision making in BC patients receiving ICT. Immune checkpoint therapy (ICT) is among the widely used treatments for breast cancer (BC), but most patients do not respond to ICT and the availability of the predictive biomarkers is limited. Emerging evidence indicates that tissue-resident macrophages (RTMs) inhibit BC progression, suggesting that their presence may predict immunotherapy response. A single-cell RNA-sequencing analysis of BC samples was performed to identify five RTM clusters with a mixed phenotype of M1-M2 macrophages. The comprehensive results showed that a high score of each RTM cluster was associated with a high infiltration of CD8+ T cells, M1 macrophages, and dendritic cells, and improved overall survival. In addition, a low score of each RTM cluster was associated with a high infiltration of M0 macrophages, naive B cells and Tregs, and poor overall survival. Gene signatures from each RTM cluster were significantly enriched in responders compared with nonresponders. Each RTM cluster expression was significantly higher in responders than in nonresponders. The analyses of bulk RNA-seq datasets of BC samples led to identification and validation of a gene expression signature, named RTM.Sig, which contained the related genes of RTM clusters for predicting response to immunotherapy. This study highlights RTM.Sig could provide a valuable tool for clinical decisions in administering ICT.

Automated summary

This study identified five tissue-resident macrophage clusters with a mixed phenotype of M1-M2 macrophages through single-cell RNA sequencing analysis. The comprehensive analysis of multi-omics data revealed that these RTM clusters exhibited an elevated inflammatory response and reactive oxygen species pathway, and were positively correlated with T cell cytotoxicity and infiltration of CD8+ T cells, indicating their potential as predictive biomarkers for immune checkpoint therapy outcomes in breast cancer patients.