头颈鳞状细胞癌 (HNSCC) 的分子特征、患者预后和治疗反应存在显着异质性，这突出表明迫切需要开发能够可靠、准确地反映肿瘤行为并为个性化治疗提供信息的分子分类。在这里，我们利用相似网络融合生物信息学方法联合分析涵盖拷贝数变异、体细胞突变、DNA 甲基化和转录组分析的多组学数据集，并得出 HNSCC 的预后分类系统。整合模型一致地确定了多个独立队列中具有特定基因组特征、生物学特征和临床结果的三个亚组 (IMC1-3)。 IMC1 亚组包括增殖性、免疫激活肿瘤，并表现出更良好的预后。 IMC2 亚型具有激活的 EGFR 信号传导和发炎的肿瘤微环境以及癌症相关的成纤维细胞/血管浸润。另外，IMC3 组的代谢活动高度异常，免疫浸润和募集受损。来自计算机预测和患者来源的异种移植模型数据的药物基因组学分析揭示了亚型特异性治疗弱点，包括 IMC1 对顺铂和免疫治疗的敏感性以及 IMC2 中的 EGFR 抑制剂 (EGFRi)，这在患者来源的类器官模型中得到了实验验证。通过机器学习开发了预后和 EGFRi 敏感性的两个特征。总之，这种针对 HNSCC 的综合多组学聚类提高了目前对肿瘤异质性的理解，并促进患者分层和针对分子脆弱性的治疗开发。

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.