PO.CL01.14 · 临床研究
整合空间转录组学和蛋白质组学分析揭示驱动透明细胞肾细胞癌异质性和耐药性的肿瘤-免疫生态位
Integrated spatial transcriptomic and proteomic profiling reveals tumor-immune niches driving heterogeneity and resistance in clear cell renal cell carcinoma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
透明细胞肾细胞癌（ccRCC）是一种由复杂的肿瘤免疫微环境相互作用所塑造的、生物学上异质性的恶性肿瘤。单一模态分析往往无法捕捉这种复杂性。空间转录组学能够实现基因表达的高分辨率定位，但蛋白质才是介导功能状态、信号传导和治疗靶点的分子。在同一组织切片上于空间转录组学之后依次应用成像质谱流式（Imaging Mass Cytometry™，IMC™）技术，通过在保留空间背景的同时定量蛋白表达和翻译后标志物，增添了一个关键层次。与基于荧光的多重技术不同，IMC技术避免了光谱重叠和自发荧光，使其成为转录组学后分析的理想选择。这种多组学方法能够将转录组特征与蛋白水平功能状态直接关联，揭示仅用转录组学仍隐藏的免疫逃逸和治疗耐药机制。采用空间转录组学（Xenium 5K检测）对一名3期ccRCC患者的FFPE肿瘤切片进行分析，随后进行苏木精-伊红（H&E）染色和采用43标志物免疫肿瘤学panel的IMC。数据经共配准以实现RNA、H&E和蛋白表达的单细胞分辨率整合。整合分析提供了ccRCC微环境前所未有的分辨率：功能亚型分型识别出代谢活跃的肿瘤细胞（高LDHA、GLUT-1）、具有促血管生成潜力的TIM-3+肿瘤群，以及空间组织化的免疫生态位，如含有B细胞、活化T细胞和巨噬细胞的三级淋巴结构（TLS）。联合分析揭示了与组织重塑和功能障碍相关的免疫细胞（CD4、CD8、CD20、CD38、TCF1、CD11c）、基质成分（alphaSMA、vimentin、fibronectin）和血管特征（CD34、PLVAP）的空间邻近性和相互作用。对RNA和蛋白数据的无监督聚类揭示了TLS中稳健的免疫激活以及具有转移潜力的分化肿瘤细胞状态。空间转录组学之后的IMC分析实现了病理学家参与（pathologist-in-the-loop）的评估，使诊断从基于形态学推进到分子指导、空间精确的肿瘤异质性和疾病进展解读。空间多组学分析提供了ccRCC中肿瘤和免疫景观前所未有的分辨率，支持空间指导的生物标志物和靶向疗法的开发。在临床上，该方法有可能精细化患者分层、识别耐药相关生态位，并指导同时靶向肿瘤和免疫区室的联合策略。仅供研究使用。不用于诊断程序。
查看英文原文 English abstract
Clear cell renal cell carcinoma (ccRCC) is a biologically heterogeneous malignancy shaped by complex tumor immune microenvironment interactions. Single-modality profiling often fails to capture this complexity. Spatial transcriptomics enables high-resolution mapping of gene expression, but proteins mediate functional states, signaling and therapeutic targets. Sequential integration of Imaging Mass Cytometry™ (IMC™) technology after spatial transcriptomics on the same tissue section adds a critical layer by quantifying protein expression and post-translational markers while preserving spatial context. Unlike fluorescence-based multiplexing, IMC technology avoids spectral overlap and autofluorescence, making it ideal for post-transcriptomic analysis. This multi-omic approach enables direct correlation of transcriptomic signatures with protein-level functional states, uncovering mechanisms of immune evasion and therapeutic resistance that remain hidden using transcriptomics alone. FFPE tumor sections from Stage 3 ccRCC patient were analyzed using spatial transcriptomics (Xenium 5K assay) followed by hematoxylin and eosin (H&E) staining and IMC with a 43-marker immuno-oncology panel. Data was coregistered for single-cell resolution integration of RNA, H&E and protein expression. Integrated analysis provided unprecedented resolution of the ccRCC microenvironment: Functional subtyping identified metabolically active tumor cells (high LDHA, GLUT-1), TIM-3+ tumor populations with angiogenic potential, and spatially organized immune niches such as tertiary lymphoid structures (TLS) containing B cells, activated T cells and macrophages. Combined profiling revealed spatial proximity and interaction of immune cells (CD4, CD8, CD20, CD38, TCF1, CD11c), stromal components (alphaSMA, vimentin, fibronectin), and vascular features (CD34, PLVAP) associated with tissue remodeling and dysfunctionality. Unsupervised clustering of RNA and protein data revealed robust immune activation in TLS and differentiated tumor cell states with metastatic potential. IMC analysis after spatial transcriptomics enables pathologist-in-the-loop evaluation, advancing diagnosis from morphology-based to molecularly informed and spatially precise interpretation of tumor heterogeneity and disease progression. Spatial multi-omic profiling delivers unprecedented resolution of tumor and immune landscapes in ccRCC, supporting development of spatially informed biomarkers and targeted therapies. Clinically, this approach could potentially refine patient stratification, identify resistance-associated niches, and guide combination strategies targeting both tumor and immune compartments. For Research Use Only. Not for use in diagnostic procedures.
利益披露 Disclosure
Q. Raza, None..
N. Zabinyakov, None.