Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer
Purpose:Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment. Experimental design: We performed high-dimensional single-cell RNA sequencing (scRNA-seq) on four patient tumors pretreatment and posttreatment from a neoadjuvant trial of patients with advanced-stage head and neck squamous cell carcinoma that were treated with the αPD-1 therapy, nivolumab. The head and neck CAF (HNCAF) protein activity profiles, derived from this cohort of paired scRNA-seq, were used to perform protein activity enrichment analysis on the 28-patient parental cohort of clinically annotated bulk transcriptomic profiles. Ex vivo coculture assays were used to test functional relevance of HNCAF subtypes. Results: Fourteen distinct cell types were identified with the fibroblast population showing significant changes in abundance following nivolumab treatment. Among the fibroblast subtypes, HNCAF-0/3 emerged as predictive of nivolumab response, while HNCAF-1 was associated with immunosuppression. Functionally, HNCAF-0/3 were found to reduce TGFβ-dependent PD-1+TIM-3+ exhaustion of CD8 T cells, increase CD103+NKG2A+ resident memory phenotypes, and enhance the overall cytolytic profile of T cells. Conclusions: Our findings demonstrate the functional importance of distinct HNCAF subsets in modulating the immunoregulatory milieu of human HNSCC. In addition, we have identified clinically actionable HNCAF subtypes that can be used as a biomarker of response and resistance in future clinical trials.
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细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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