Integrative multi-omics analyses identify molecular subtypes of head and neck squamous cell carcinoma with distinct therapeutic vulnerabilities

Substantial heterogeneity in molecular features, patient prognoses, and therapeutic responses in head and neck squamous cell carcinomas (HNSCC) highlights the urgent need to develop molecular classifications that reliably and accurately reflect tumor behavior and inform personalized therapy. Here, we leveraged the similarity network fusion bioinformatics approach to jointly analyze multi-omics datasets spanning copy number variations, somatic mutations, DNA methylation, and transcriptomic profiling and derived a prognostic classification system for HNSCC. The integrative model consistently identified three subgroups (IMC1-3) with specific genomic features, biological characteristics, and clinical outcomes across multiple independent cohorts. The IMC1 subgroup included proliferative, immune-activated tumors and exhibited a more favorable prognosis. The IMC2 subtype harbored activated EGFR signaling and an inflamed tumor microenvironment with cancer-associated fibroblast/vascular infiltrations. Alternatively, the IMC3 group featured highly aberrant metabolic activities and impaired immune infiltration and recruiting. Pharmacogenomics analyses from in silico predictions and from patient-derived xenograft model data unveiled subtype-specific therapeutic vulnerabilities including sensitivity to cisplatin and immunotherapy in IMC1 and EGFR inhibitors (EGFRi) in IMC2, which was experimentally validated in patient-derived organoid models. Two signatures for prognosis and EGFRi sensitivity were developed via machine learning. Together, this integrative multi-omics clustering for HNSCC improves current understanding of tumor heterogeneity and facilitates patient stratification and therapeutic development tailored to molecular vulnerabilities. Gene expression profiling analysis of RNA-seq data for organoids from 13 patients with head neck squamous cell carcinoma (HNSCC).

头颈部鳞状细胞癌（head and neck squamous cell carcinomas, HNSCC）在分子特征、患者预后及治疗应答方面存在显著异质性，这凸显出开发能够可靠且精准反映肿瘤生物学行为、指导个体化治疗的分子分型体系的迫切需求。本研究借助相似性网络融合（similarity network fusion, SNF）生物信息学方法，对涵盖拷贝数变异、体细胞突变、DNA甲基化及转录组谱的多组学数据集进行联合分析，构建了头颈部鳞状细胞癌的预后分型体系。该整合模型在多个独立队列中均稳定识别出三个具有独特基因组特征、生物学特性及临床结局的亚组（IMC1-3）。IMC1亚组对应增殖活跃、免疫激活型肿瘤，预后相对更佳；IMC2亚型存在激活的表皮生长因子受体（epidermal growth factor receptor, EGFR）信号通路，且肿瘤微环境呈炎症状态，伴癌相关成纤维细胞/血管浸润；而IMC3亚组则以代谢活动显著异常、免疫浸润与招募功能受损为特征。通过虚拟预测及患者来源异种移植瘤（patient-derived xenograft, PDX）模型数据开展的药物基因组学分析，揭示了各亚组特异性的治疗脆弱性：IMC1亚组对顺铂及免疫治疗敏感，IMC2亚组对EGFR抑制剂（EGFRi）敏感，上述结论均在患者来源类器官（organoid）模型中得到实验验证。本研究通过机器学习方法构建了用于预后预测及EGFRi敏感性评估的两个特征标签。综上，这款针对头颈部鳞状细胞癌的整合多组学聚类分析方法，加深了当前对肿瘤异质性的认知，同时有助于实现患者分层及针对分子脆弱性的治疗方案开发。本数据集涵盖13例头颈部鳞状细胞癌患者来源类器官的RNA测序（RNA-seq）数据的基因表达谱分析内容。