Single-cell and spatial transcriptomics to analyze radiation therapy-induced tumor microenvironment reshaping in HPV- cervical cancer.

肿瘤微环境 放射治疗 转录组 医学 癌症研究 细胞 间质细胞 免疫系统 生物 病理 免疫学 内科学 遗传学 基因 基因表达
作者
Yan Zhang,Zhiyuan Xu,Bin Ye,Danyang Zheng,Cai‐Ning Zhao,Wanli Xu,Lingyu Ma,Zhibing Liang,Xiangyu Xiao,Yang Li,Hao Yu,Feng‐Ming Kong
出处
期刊:Journal of Clinical Oncology [Lippincott Williams & Wilkins]
卷期号:41 (16_suppl): e17528-e17528
标识
DOI:10.1200/jco.2023.41.16_suppl.e17528
摘要

e17528 Background: Radiotherapy (RT) is the most widely used conventional first-line treatment in cervical cancer (CC). However, the treatment response rate was only 10%-40%, and even lower in HPV- negative CC (HPV- CC). So far, previous knowledge of radiotherapy on tumor and tumor immune microenvironment (TME) remodeling is still insufficient. In this study, we aimed to comprehensively understand the dynamic change of tumor and TME of HPV- CC at the single-cell level. Methods: The single-cell and spatial transcriptome sequencing (STRNAseq) were used to analyze the TME of newly diagnosed HPV- CC tumor tissues, including pre-radiotherapy and 3 weeks after radiotherapy. Tissue samples were split into two parts, the freshly lysed single-cell suspension for 10X single-cell sequencing, and another part that was immediately processed into frozen and embedded for STRNAseq. Cell clustering, annotation and Single-cell sequencing and spatial transcriptome integration were analyzed by Seurat 4.0. The CNV of epithelial cells (ECs) was estimated by InferCNV. Results: A total of 14000 cells were obtained from paired HPV-CC patient samples (pre-RT and 3 weeks after RT) and clustered into 21 cell subsets. Six EC subsets, four fibroblast subsets, three macrophage subsets, two T cell subsets, two B cell subsets, one mast cell cluster, and an endothelial cell cluster were discovered. CNV analysis revealed that all ECs were malignantly proliferating tumor cells. Tumor cells accounted for 90% of the total tumor tissue prior to RT, but only less than 1% of tumor cells remain after RT. Fibroblasts and myeloid cells, which make up 60% of tumor tissue, increase significantly after radiotherapy, with myeloid cells accounting for nearly 50% of the total. Furthermore, we found a population of cells in the Post-RT sample that were both highly expressed CD14 and CD68 (macrophage biomarkers) and highly expressed COL1A2 (fibroblast biomarker). Differential analysis showed that this group of cells was down-regulated in Focal adhesion and ECM-receptor interaction pathways, while up-regulated in cytokine secretion and phagosome pathways, indicating that this group of cells might remodel the intercellular matrix and interacted with other cells to regulate the immune microenvironment. Integrating single-cell and spatial transcriptome data, fibroblasts and myeloid cells formed a tight co-distribution in tissues after radiotherapy, implying that their interaction is critical for the formation of the tumor immune microenvironment. Conclusions: This study comprehensively analyzed the dynamic change of tumor and TME of HPV- CC treated with RT at the single-cell transcriptional scale. After radiotherapy, fibroblasts and myeloid cells significantly increased and were highly heterogeneous, and the interaction between them is critical in forming a tumor ecosystem for radiotherapy.

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