Integrative Single-Cell and Bulk Transcriptomes Analyses Identify Intrinsic HNSCC Subtypes with Distinct Prognoses and Therapeutic Vulnerabilities

Abstract

Abstract Purpose: Tumor heterogeneity in head and neck squamous cell carcinoma (HNSCC) profoundly compromises patient stratification, personalized treatment planning, and prognostic prediction, which underscores the urgent need for more effective molecular subtyping for this malignancy. Here, we sought to define the intrinsic epithelial subtypes for HNSCC by integrative analyses of single-cell and bulk RNA sequencing datasets from multiple cohorts and assess their molecular features and clinical significance. Experimental Design: Malignant epithelial cells were identified from single-cell RNA sequencing (scRNA-seq) datasets and subtyped on the basis of differentially expressed genes. Subtype-specific genomic/epigenetic abnormalities, molecular signaling, genetic regulatory network, immune landscape, and patient survival were characterized. Therapeutic vulnerabilities were further predicted on the basis of drug sensitivity datasets from cell lines, patient-derived xenograft models, and real-world clinical outcomes. Novel signatures for prognostication and therapeutic prediction were developed by machine learning and independently validated. Results: Three intrinsic consensus molecular subtypes (iCMS1–3) for HNSCC were proposed from scRNA-seq analyses and recapitulated in 1,325 patients from independent cohorts using bulk-sequencing datasets. iCMS1 was characterized by EGFR amplification/activation, stromal-enriched environment, epithelial-to-mesenchymal transition, worst survival, and sensitivities to EGFR inhibitor. iCMS2 was featured by human papillomavirus–positive oropharyngeal predilection, immune-hot, susceptibilities to anti–PD-1, and best prognosis. Moreover, iCMS3 displayed immune-desert and sensitivities to 5-FU and MEK, STAT3 inhibitors. Three novel, robust signatures derived from iCMS subtype-specific transcriptomics features were developed by machine learning for patient prognostication and cetuximab and anti–PD-1 response predictions. Conclusions: These findings reiterate molecular heterogeneity of HNSCC and advantages of scRNA-seq in pinpointing cellular diversities in complex cancer ecosystems. Our HNSCC iCMS regime might facilitate accurate patient stratification and individualized precise treatment.