Xiao Lei, Shen Zhe, Pan Zhaoyu, Qiu Yuanzheng, Huang Donghai, Liu Yong, Liu Chao, Zhang Xin
Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China.
J Transl Med. 2025 Mar 1;23(1):254. doi: 10.1186/s12967-025-06299-4.
Characterizing the variety of cell types in the tumor microenvironment (TME) and their organization into cellular communities is vital for elucidating the biological diversity of cancer and informing therapeutic strategies.
Here, we employed a machine learning-based algorithm framework, EcoTyper, to analyze single-cell transcriptomes from 139 patients with head and neck squamous cell carcinoma (HNSC)and gene expression profiles from 983 additional HNSC patients, aiming to delineate the fundamental cell states and ecosystems integral to HNSC.
A diverse landscape of 66 cell states and 9 ecosystems within the HNSC microenvironment was identified, revealing classical cell types while also expanding upon previous immune classifications. Survival analysis revealed that specific cell states and ecotypes (ecosystems) are associated with patient prognosis, underscoring their potential as indicators of clinical outcomes. Moreover, distinct cell states and ecotypes exhibited varying responses to immunotherapy and chemotherapy, with several showing promise as predictive biomarkers for treatment efficacy.
Our large-scale integrative transcriptome analysis provides high-resolution insights into the cellular states and ecosystems of HNSC, facilitating the discovery of novel biomarkers and supporting the development of precision therapies.
表征肿瘤微环境（TME）中细胞类型的多样性及其组成细胞群落的方式，对于阐明癌症的生物多样性和指导治疗策略至关重要。
在此，我们采用了一种基于机器学习的算法框架EcoTyper，来分析139例头颈部鳞状细胞癌（HNSC）患者的单细胞转录组以及另外983例HNSC患者的基因表达谱，旨在描绘出HNSC所特有的基本细胞状态和生态系统。
在HNSC微环境中识别出了由66种细胞状态和9种生态系统构成的多样格局，揭示了经典细胞类型，同时也扩展了以往的免疫分类。生存分析表明，特定的细胞状态和生态型（生态系统）与患者预后相关，突显了它们作为临床结果指标的潜力。此外，不同的细胞状态和生态型对免疫疗法和化疗表现出不同的反应，其中几种显示出有望成为治疗疗效的预测生物标志物。
我们的大规模综合转录组分析为HNSC的细胞状态和生态系统提供了高分辨率的见解，有助于发现新的生物标志物并支持精准疗法的开发。
