PO.BCS01.16 · 生物信息与计算
一个全面的头颈部鳞状细胞癌单细胞图谱定义了恶性细胞状态并使得能够对既往的大宗转录组队列进行去卷积
A comprehensive single-cell atlas of head and neck squamous cell carcinoma defines malignant cell states and enables deconvolution of legacy bulk transcriptomic cohorts
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:头颈部鳞状细胞癌(HNSCC)的特征是由多样的恶性细胞状态所驱动的瘤内异质性,以及受HPV感染和烟草暴露影响的复杂肿瘤微环境。既往研究已使用单细胞RNA测序(scRNAseq)来探索这种复杂性,但尚无一项研究提供了一个大规模、整合、经过严格筛选和系统验证、具有高分辨率细胞亚型注释的图谱。现有的scRNAseq资源缺乏与包含深度临床特征和长期结局的大宗既往转录组队列的直接联系。
方法:我们筛选、注释并整合了来自7项HNSCC研究的scRNAseq数据集,涵盖所有疾病分期的137名个体。在严格的质量控制和批次校正后,细胞被分类为主要细胞类型和细胞状态。随后对上皮细胞进行亚聚类,以定义转录细胞状态和相关的标志性通路活性。利用这些图谱衍生的恶性和微环境状态,我们构建了一个贝叶斯模型来预测各个细胞类型内的细胞组成。该模型随后被应用于对24个大宗转录组HNSCC队列中1705个样本进行去卷积,从而能够估计细胞状态比例及其与HPV状态、酒精和烟草暴露以及临床结局的关联。
结果:对368,842个高质量细胞进行聚类,识别出16个主要细胞类型中的63种细胞状态。在恶性上皮细胞群体中,我们定义了12种不同的状态,包括染色质重塑、纤毛、E2F靶点、EMT-II、细胞周期时相和应激反应。使用BayesPrism方法对大宗队列进行去卷积重现了这些图谱定义的特征,并揭示了跨数据集的可重复临床关联。较低的EMT-II(p=0.02)、较高的谷胱甘肽(p=0.038)、增多的cDC3(p=0.024)以及升高的IFN-TAM(p=0.0153)与生存改善相关。HPV状态与不同的细胞状态显示出显著关联,包括CD4 Tcm、CD8 Temra、增殖性成纤维细胞、G2M上皮细胞、肌成纤维细胞以及TNFRSF9+ Treg细胞。
结论:本研究建立了一个全面的HNSCC单细胞图谱,并引入了一个基于BayesPrism的框架,用于将恶性和微环境细胞状态投射到异质性的大宗转录组数据集上。通过将高分辨率的细胞图谱与大规模、临床注释的队列相联系,我们的工作提供了一种可扩展且稳健的策略,以解码HNSCC的生态系统异质性。
查看英文原文 English abstract
Background: Head and neck squamous cell carcinoma (HNSCC) is characterized by intratumor heterogeneity driven by diverse malignant cell states and a complex tumor microenvironment influenced by HPV infection and tobacco exposure. Previous studies have used single-cell RNA sequencing (scRNAseq) to explore this complexity, none has delivered a large-scale, integrated, rigorously curated, and systematically validated atlas with high-resolution cell subtype annotation. Existing scRNAseq resources lack direct links to large legacy bulk transcriptomic cohorts that contain deep clinical characterization and long-term outcomes.
Methods: We curated, annotated,integrated scRNAseq datasets from 7 HNSCC studies, comprising 137 individuals across all disease stages. After stringent quality control and batch correction, cells were classified into major cell type and cell states. Epithelial cells were then subclustered to define transcriptional cell states and associated hallmark pathway activities. Using these atlas-derived malignant and microenvironmental states, we constructed a Bayesian model to predict cellular composition within individual cell types. This model was then applied for deconvolution of 1705 samples in 24 bulk transcriptomic HNSCC cohorts, enabling estimation of cell-state proportions and associations with HPV status, alcohol and tobacco exposure, and clinical outcomes.
Results: Clustering of 368,842 high-quality cells identified 63 cell states across 16 major cell types. Within the malignant epithelial population, we defined 12 distinct states including chromatin remodeling, cilia, E2F targets, EMT-II, cell cycle phases, and stress responses. Deconvolution of bulk cohorts using the BayesPrism approach recapitulated these altas-defined signatures and uncovered reproducible clinical association across datasets. Lower EMT-II (p=0.02), higher glutathione (p=0.038), increased cDC3 (p=0.024), and elevated IFN-TAM (p=0.0153) were associated with improved survival. HPV status demonstrated significant associations with distinct cell states including CD4 Tcm, CD8 Temra, proliferative fibroblast, G2M epithelial cells, myofibroblasts, and TNFRSF9+ Treg cells.
Conclusions: This study establishes a comprehensive single-cell atlas for HNSCC and introduces a BayesPrism-powered framework for projecting malignant and microenvironmental cell states onto heterogeneous bulk transcriptomic datasets. By linking high-resolution cellular profiles with large, clinically annotated cohorts, our work provides a scalable and robust strategy to decode the ecosystem heterogeneity of HNSCC.
利益披露 Disclosure
Y. Shen, None..
T. Li, None..
X. Yu, None.