基于质谱流式细胞技术的乳腺癌免疫微环境预后特征分析

doi:10.3969/j.issn.1674-8115.2026.01.004

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (1): 34-42.doi: 10.3969/j.issn.1674-8115.2026.01.004

基于质谱流式细胞技术的乳腺癌免疫微环境预后特征分析

章玥, 陈青建()

上海交通大学医学院附属第一人民医院肿瘤中心，上海 200080

收稿日期:2025-04-01 接受日期:2025-08-16 出版日期:2026-01-28 发布日期:2026-01-30
通讯作者: 陈青建，助理研究员，博士；电子信箱：chenqingjian2010@163.com。
作者简介:第一联系人：章玥负责实验设计、实验操作、论文写作和修改，陈青建负责课题设计、生物信息学分析和论文审阅。两位作者均阅读并同意了最终稿件的提交。
基金资助:
国家自然科学基金(82002470)

Mass cytometry reveals prognostic immune microenvironment features in breast cancer

Zhang Yue, Chen Qingjian()

Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China

Received:2025-04-01 Accepted:2025-08-16 Online:2026-01-28 Published:2026-01-30
Contact: Chen Qingjian, E-mail: chenqingjian2010@163.com.
About author:First author contact:Zhang Yue was responsible for experimental design, experimental operations, and manuscript writing and revision. Chen Qingjian was responsible for project design, bioinformatics analysis, and manuscript revision. Both authors have read the last version of paper and consented to submission.
Supported by:
National Natural Science Foundation of China(82002470)

摘要/Abstract

摘要：

目的·使用质谱流式细胞技术（cytometry by time-of-flight，CyTOF）分析乳腺癌患者肿瘤组织的多种抗原，探究其与乳腺癌微环境、乳腺癌患者预后的关系。方法·使用Maxpar^® Panel Designer v2.0.1软件结合相关抗原蛋白及组织细胞标志物设计Panel。使用Maxpar X8抗体标记试剂盒将相关镧系（Ln）金属同位素与Panel中的蛋白抗体连接后，采用成像质谱流式细胞染色（imaging mass cytometry staining，IMC）法对乳腺癌组织芯片进行染色。在Hyperion成像系统中观察，得到Panel中多种蛋白标志物的表达和空间定位。使用R语言对原始数据进行数据归一化、去噪和降噪、补偿校正以及数据转换，再进行降维处理。通过聚类算法进行细胞亚群注释。使用空间邻域分析，解析乳腺癌微环境中的各类细胞和临床意义。结果·将金属标签与相应的抗原抗体连接后，染色效果良好，可用于IMC染色。在乳腺癌组织芯片中，根据现有的26种标志物可以将乳腺癌微环境分成9种细胞类型，共410 000个细胞。在配对的肿瘤组织和癌旁组织中，乳腺癌微环境主要由B细胞、CD4⁺ T细胞、CD8⁺ T细胞、上皮细胞、内皮细胞、巨噬细胞、肌上皮细胞、中性粒细胞、成纤维细胞组成。其中，巨噬细胞和CD4⁺ T细胞在肿瘤组织与癌旁组织中的数量差异有统计学意义（均P<0.05）。在乳腺癌组织中鉴定出15种特征性细胞邻域，其中CD8⁺ T细胞和巨噬细胞与肿瘤细胞空间共定位邻域，与患者生存期延长显著相关（P=0.011，P<0.001）。结论·CyTOF对于大批量检测多种抗原在组织中的表达有重要作用，可以在微观角度上分析乳腺癌组织与乳腺癌微环境的关系。在乳腺癌微环境中，CD8⁺ T细胞和巨噬细胞的表达量较高与乳腺癌患者的良好预后相关。

关键词: 乳腺癌, 质谱流式细胞技术, 肿瘤微环境

Abstract:

Objective ·To analyze the expression of multiple antigens in tumor tissues from breast cancer patients using cytometry by time-of-flight (CyTOF), with the aim of investigating their associations with the tumor microenvironment and patient prognosis. Methods ·A panel of optimally combined metal-labeled antibodies (or probes) was designed by employing Maxpar^® Panel Designer v2.0.1 software in conjunction with relevant antigenic proteins and histiocytic markers. Antibodies targeting panel proteins were conjugated with lanthanide (Ln) metal isotopes using the Maxpar X8 Antibody Labeling Kit. Breast cancer tissue microarrays were then stained using imaging mass cytometry staining (IMC). Protein expression profiles and spatial distributions were acquired using the Hyperion Imaging System. Raw data were processed using R, including data normalization, noise reduction/removal, signal compensation, transformation, and dimensionality reduction. Cellular subpopulations were annotated via clustering algorithms, while spatial neighborhood analysis was performed to map the spatial organization of diverse cell types within the breast cancer microenvironment and assess their clinical relevance. Results ·Successful metal-antibody conjugation resulted in high-quality staining suitable for IMC analysis. Analysis of breast cancer tissue microarrays using 26 markers identified nine distinct cell populations (410 000 cells) within the tumor microenvironment. Paired comparisons of tumor and adjacent tissues revealed that the microenvironment predominantly consisted of B cells, CD4⁺ T cells, CD8⁺ T cells, epithelial cells, endothelial cells, macrophages, myoepithelial cells, neutrophils, and fibroblasts. Quantitative analysis showed statistically significant differences in the abundance of macrophages and CD4⁺ T cells between malignant and adjacent tissues (P<0.05). Spatial analysis identified 15 distinct cellular neighborhoods, and the colocalization of CD8⁺ T cells and macrophages with tumor cells was significantly associated with improved patient survival (P=0.011, P<0.001). Conclusion ·CyTOF is a powerful tool for high-throughput detection of multiple antigens in tissue samples, enabling detailed analysis of tumor-immune interactions in the breast cancer microenvironment. The presence and spatial organization of CD8⁺ T cells and macrophages within the breast cancer microenvironment are positively associated with favorable patient outcomes.

Key words: breast cancer, cytometry by time-of-flight (CyTOF), tumor microenvironment

中图分类号:

R737.9

章玥, 陈青建. 基于质谱流式细胞技术的乳腺癌免疫微环境预后特征分析[J]. 上海交通大学学报（医学版）, 2026, 46(1): 34-42.

Zhang Yue, Chen Qingjian. Mass cytometry reveals prognostic immune microenvironment features in breast cancer[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(1): 34-42.

图/表 4

表1 乳腺癌免疫微环境相关Panel

Tab 1 Panel for profiling breast cancer immune microenvironment

Metal label	Protein marker
141Pr	SMA
142Nd	CD14
144Nd	GAPDH
145Nd	CD11C
146Nd	CD16
147Sm	CD68
148Nd	CD15
149Sm	CD57
150Nd	CD25
152Sm	CD45
156Gd	PD1
158Gd	CDH1
159Tb	CD206
160Gd	CD24
162Dy	CD8a
163Dy	CD83
164Dy	CD11A
165Ho	CD4
166Er	HIF1A
167Er	ALDH1
168Er	Ki67
169Tm	CD31
171Yb	CD27
172Yb	CD66b
173Yb	CD62L
176Yb	CD3

图1 改进实验条件前后IHC与IMC预实验效果的比较Note： A. Staining effect of CD31 with different antibody concentrations on breast cancer tissues. B. Data quality of Ki67 with different antibody concentrations.

Fig 1 Comparison of pre-experimental effects of IHC and IMC before and after improving experimental conditions

图2 IMC分析乳腺癌免疫微环境的组成及免疫相关分子的空间分布Note： A. Schematic diagram of IMC workflow. B. Flowchart of IMC raw data processing and bioinformatics analysis. C. Differential expression profiles of distinct immune cell subsets between tumor tissues and normal tissues. D. Expression profiles of partial classical markers across distinct immune cell subsets.

Fig 2 Analysis of composition of breast cancer immune microenvironment and spatial distribution of immune-related molecules

图3 细胞邻域法分析乳腺癌免疫微环境的功能Note： A. Cellular distribution exhibits distinct heterogeneity across different tissue niches. B. The predominance of CD8⁺ T cells in CN7 is significantly correlated with the prognosis of breast cancer patients. C. The predominance of macrophages in CN8 is significantly correlated with the prognosis of breast cancer patients.

Fig 3 Functional analysis of the breast cancer immune microenvironment using the cell neighborhood method

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基于质谱流式细胞技术的乳腺癌免疫微环境预后特征分析

Mass cytometry reveals prognostic immune microenvironment features in breast cancer

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