Single-Cell Transcriptomics Identifies Novel Prognostic Signatures in HNSCC Immunotherapy Response.

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Head and neck squamous cell carcinoma (HNSCC) presents significant therapeutic challenges.
• Understanding the tumor immune microenvironment is crucial for effective immunotherapy.
• Predictive models are needed to guide HNSCC treatment strategies.

Purpose of the Study:

• To analyze changes in the tumor immune microenvironment of HNSCC patients during immunotherapy.
• To develop a prognostic model for immunotherapy response in HNSCC.
• To identify immune cell signatures associated with treatment outcomes.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) data from HNSCC patients treated with TLR8 agonist and anti-PD1 therapy were analyzed.
• Cell subpopulations and immune cell types (six major types) were identified before and after treatment.
• A LASSO-Cox risk stratification model was developed using scRNA-seq and bulk RNA-seq data.

Main Results:

• 19 pre-treatment and 13 post-treatment cell subpopulations were identified.
• Differential gene expression patterns were observed in major immune cell types.
• A 51-gene LASSO-Cox model demonstrated robust predictive performance (AUC: 0.749-0.800) for patient survival.
• High-risk patients showed increased myeloid-derived suppressor cells (MDSCs) and cancer-associated fibroblasts (CAFs), and altered immune cell infiltration.

Conclusions:

• The developed prognostic model effectively captures patient immune status in HNSCC.
• The model provides insights for optimizing immunotherapy strategies in HNSCC.
• Identifying immune cell signatures can improve treatment selection and patient outcomes.