Integration of individual sEV with single-cell analysis to reveal association of immune cell subsets with chemotherapy response in breast cancer.

Abstract

e13114 Background: Small extracellular vesicles (sEVs) released by cancer cells directly interact with various immune cell types to mediate immune dysfunction in the tumor microenvironment (TIME). Membrane surface proteins on sEV specifically contact with the immune cells by receptor-receptor or ligand-receptor pairs. Chemotherapy is a crucial treatment of BC, while can be significantly regulated by immune cells. However, the precise impact of sEVs on immune cell activities in subjects who are resistant to chemotherapy remains unclear. Methods: We discovered that sEVs membrane surface proteins exhibit complex and diverse biological activities within breast cancer TIME, especially its interaction with macrophages. By integrating sEV surface protein data with breast cancer single-cell RNA-sequencing (scRNA-seq) and bulk RNA sequencing data, we have identified the crucial ligand-receptor interactions involving integrin ITGB2 and intercellular adhesion molecule ICAM1, which has the potential to significantly influence the activities of macrophages that mediate chemotherapy resistance. Results: Notably, the sEV surface proteins integrin ITGB2, and intercellular adhesion molecule ICAM1 are found to be elevated in sEVs derived from the tumor tissues of TNBC and Luminal A breast cancer patients. ITGB2 and ICAM1 mRNA levels are significantly increased in chemotherapy nonresponsive subjects who also have shorter survival time. More importantly, M0 macrophage is identified to be a risk factor of chemotherapy nonresponse. The expressions of ITGB2 and ICAM1 are correlated with M0 and M2 marker expressions, and the VCAM1-mediated signaling pathway. Based on the characteristics of ICAM1-ITGB2 interaction related genes between BC-derived sEVs and macrophages, we have established the chemotherapy prognosis signature. Moreover, the potential molecules for targeting ICAM1-ITGB2 in chemotherapy resistance have been identified by DGIDB database and molecular autodocking. Conclusions: Identification of crucial sEVs-cells interaction not only enables us to gain a more precise understanding of the pathological mechanism network, but also allows us to develop innovative therapeutic and diagnostic strategies to overcome the BC chemotherapy resistance.