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

◥ ABSTRACT Purpose: Tumor heterogeneity in head and neck squamous cell carcinoma (HNSCC) profoundly compromises patient stratifi- cation, personalized treatment planning, and prognostic predic-tion, 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 sub -typed 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 signa-tures for prognostication and therapeutic prediction were devel-oped 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 sensi-tivities to EGFR inhibitor. iCMS2 was featured by human pap-illomavirus-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 devel-oped by machine learning for patient prognostication and cetux-imab 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 individ-ualized precise treatment.