bioRxivCancer BiologyPreprint
Targeting CD74-positive macrophages improves neoadjuvant therapy in cervical cancer as revealed by single-cell transcriptomics analysis
Z. WangiD, B. Wang, Y. Feng, J. Ye, Z. Mao, T. Zhang, M. Xu, W. Zhang, X. Jiao, Y. Zhang, B. Cui
Abstract
Uterine cervical cancer (UCC) has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic strategy for cervical cancer. However, the efficacy of PD-1 (programmed cell death protein 1) blockade combined with neoadjuvant chemotherapy (NACT) and the ensuing alterations within the tumor microenvironment need further investigation. We conducted single-cell RNA sequencing using 46,950 cells from nine sequential human cervical cancer tissues representing different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and unveiled crucial factors involved in the combination therapy. Cell communication analysis revealed the inferred interaction strength decreased between T cell-cancer cells, while the communication between macrophage-cancer cells intensified after NACT therapy. We verified that macrophages are necessary for PD-1 blockade combination to exert antitumor effects in vitro. Detailed analysis unraveled the CD74-positive macrophages frequently interacted with the immunoreactive Epi 3 subgroup during neoadjuvant combination therapy. CD74 upregulation limited phagocytosis and stimulated M2 polarization. The CD74 blockade enhanced macrophage phagocytosis, resulting in decreased viability of cervical cancer cells in vitro and in vivo. This study unveils the dynamic microenvironment of UCC influenced by NACT and PD-1 blockade combination therapy, and targeting CD74 was a potential strategy to augment the therapy efficacy.
- organism: human
- assay: scRNA-seq
- research focus: cell-cell communication
- research focus: differential expression
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
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