Dai Yibin, Wang Ziyu, Xia Yingchao, Li Jin, Wu Yaping, Wang Yanling, Jiang Hongbing, Cheng Jie
Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Jiangsu, China.
Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, China.
Clin Cancer Res. 2023 Aug 1;29(15):2845-2858. doi: 10.1158/1078-0432.CCR-22-3563.
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.
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.
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.
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.
头颈部鳞状细胞癌（HNSCC）中的肿瘤异质性严重影响患者分层、个性化治疗规划和预后预测，这凸显了对这种恶性肿瘤进行更有效分子分型的迫切需求。在此，我们试图通过对来自多个队列的单细胞和批量RNA测序数据集进行综合分析，来定义HNSCC的内在上皮亚型，并评估其分子特征和临床意义。
从单细胞RNA测序（scRNA-seq）数据集中鉴定出恶性上皮细胞，并根据差异表达基因进行亚型分类。对亚型特异性基因组/表观遗传异常、分子信号传导、基因调控网络、免疫格局和患者生存情况进行了表征。基于细胞系、患者来源的异种移植模型的药物敏感性数据集以及实际临床结果，进一步预测了治疗易感性。通过机器学习开发了用于预后和治疗预测的新特征，并进行了独立验证。
通过scRNA-seq分析提出了HNSCC的三种内在一致性分子亚型（iCMS1-3），并使用批量测序数据集在来自独立队列的1325名患者中进行了验证。iCMS1的特征是表皮生长因子受体（EGFR）扩增/激活、富含基质的环境、上皮-间质转化、最差的生存率以及对EGFR抑制剂敏感。iCMS2的特点是人类乳头瘤病毒阳性口咽癌倾向、免疫激活、对抗程序性死亡蛋白1（PD-1）敏感以及最佳预后。此外，iCMS3表现为免疫逃逸，对5-氟尿嘧啶（5-FU）、丝裂原活化蛋白激酶（MEK）和信号转导子和转录激活子3（STAT3）抑制剂敏感。通过机器学习从iCMS亚型特异性转录组学特征中开发了三种新的、强大的特征，用于患者预后以及西妥昔单抗和抗PD-1反应预测。
这些发现重申了HNSCC的分子异质性以及scRNA-seq在确定复杂癌症生态系统中细胞多样性方面的优势。我们的HNSCC iCMS分类法可能有助于准确的患者分层和个体化精准治疗。
