bioRxivCancer BiologyPreprint
Integrated single-cell and spatial transcriptomic analysis of T cell exhaustion and immunometabolic remodeling in HPV-positive oropharyngeal squamous cell carcinoma
Ke WangiD, Nian Hui Deng, Yueqin Tao, Xin YangiD, Mujie YuaniD, Longhua Gao, Shuai Jiang, Wei ShangiD, Jing Deng, Lin Wang
Abstract
HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) harbors dense lymphocytic infiltration yet responds poorly to immune checkpoint blockade (ICB), a paradox whose mechanistic basis remains unresolved. Here we constructed an integrated single-cell and spatial transcriptomic atlas of HPV OPSCC (23,119 cells, 24 cell types), complemented by CRISPR-Cas9 functional validation in two HPV cell lines and multiplex immunofluorescence across independent cases. Pseudotime trajectory analysis placed TOXCD8 T cells, rather than canonically defined exhausted cells, at the terminal exhaustion endpoint, and uncovered a previously uncharacterized IEGCD8 effector memory population at a fate-decision juncture between activation and irreversible exhaustion. Actively cycling Tregs with broad co-stimulatory and co-inhibitory receptor expression underwent intratumoral immunosuppressive differentiation. Compartmentalized LDHA/MCT4 expression in cancer cells versus LDHB/MCT1 in T cells established a directional lactate flux model, and spatial deconvolution confirmed peripheral immune exclusion. We identified a KMT2D-KLF7-PD-L1 regulatory axis: KMT2D sustains KLF7 transcription through H3K4me1-dependent enhancer activation; KLF7 concurrently drives neural crest differentiation and upregulates PD-L1, thereby linking epigenetic remodeling to both neural programming and immune checkpoint expression. These findings define converging multi-scale mechanisms underlying ICB resistance in HPV OPSCC and nominate the KMT2D-KLF7-PD-L1 axis as a combinatorial therapeutic target.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
- 0
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