Mass cytometry reveals prognostic immune microenvironment features in breast cancer

doi:10.3969/j.issn.1674-8115.2026.01.004

Abstract

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

CLC Number:

R737.9

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.

Figures/Tables 4

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

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

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

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

TrendMD

References 35

[1]	Dvir K, Giordano S, Leone J P. Immunotherapy in breast cancer[J]. Int J Mol Sci, 2024, 25(14): 7517.
[2]	Xiong X, Zheng L W, Ding Y, et al. Breast cancer: pathogenesis and treatments[J]. Signal Transduct Target Ther, 2025, 10(1): 49.
[3]	Vandereyken K, Sifrim A, Thienpont B, et al. Methods and applications for single-cell and spatial multi-omics[J]. Nat Rev Genet, 2023, 24(8): 494-515.
[4]	Pandey A, Bhutani N. Profiling joint tissues at single-cell resolution: advances and insights[J]. Nat Rev Rheumatol, 2024, 20(1): 7-20.
[5]	Rai M F, Wu C L, Capellini T D, et al. Single cell omics for musculoskeletal research[J]. Curr Osteoporos Rep, 2021, 19(2): 131-140.
[6]	Arnett L P, Rana R, Chung W W, et al. Reagents for mass cytometry[J]. Chem Rev, 2023, 123(3): 1166-1205.
[7]	Glasson Y, ChéPeaux L A, Dumé A S, et al. Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology[J]. Semin Immunopathol, 2023, 45(1): 17-28.
[8]	Kante A, Chevalier M F, SèNe D, et al. Mass cytometry: exploring the immune landscape of systemic autoimmune and inflammatory diseases in the past fourteen years[J]. Front Immunol, 2024, 15: 1509782.
[9]	Le Rochais M, Hemon P, Pers J O, et al. Application of high-throughput imaging mass cytometry hyperion in cancer research[J]. Front Immunol, 2022, 13: 859414.
[10]	Naderi-Azad S, Croitoru D, Khalili S, et al. Research techniques made simple: experimental methodology for imaging mass cytometry[J]. J Invest Dermatol, 2021, 141(3): 467-473.e1.
[11]	Gray G K, Li C M, Rosenbluth J M, et al. A human breast atlas integrating single-cell proteomics and transcriptomics[J]. Dev Cell, 2022, 57(11): 1400-1420.e7.
[12]	Fan Y Y, Kao C Y, Yang F, et al. Integrated multi-omics analysis model to identify biomarkers associated with prognosis of breast cancer[J]. Front Oncol, 2022, 12: 899900.
[13]	Wang X, Chai Y Y, Quan Y, et al. NPM1 inhibits tumoral antigen presentation to promote immune evasion and tumor progression[J]. J Hematol Oncol, 2024, 17(1): 97.
[14]	Li Y, Jiang M L, Ling A Y, et al. UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment[J]. Nat Commun, 2024, 15(1): 1200.
[15]	Liu H F, Zhao Q W, Tan L Y, et al. Neutralizing IL-8 potentiates immune checkpoint blockade efficacy for glioma[J]. Cancer Cell, 2023, 41(4): 693-710.e8.
[16]	Ma J L, Pang Y H, Shang Y F, et al. CyTOF analysis revealed platelet heterogeneity in breast cancer patients received T-DM1 treatment[J]. Clin Immunol, 2024, 263: 110227.
[17]	Lo Y C, Keyes T J, Jager A, et al. CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors[J]. Nat Commun, 2022, 13(1): 934.
[18]	Rybakowska P, Van Gassen S, Quintelier K, et al. Data processing workflow for large-scale immune monitoring studies by mass cytometry[J]. Comput Struct Biotechnol J, 2021, 19: 3160-3175.
[19]	Liu X, Song W C, Wong B Y, et al. A comparison framework and guideline of clustering methods for mass cytometry data[J]. Genome Biol, 2019, 20(1): 297.
[20]	Li X H, Zhang L, Liu C C, et al. Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma[J]. Biofactors, 2025, 51(1): e2135.
[21]	Artyomov M N, Van Den Bossche J. Immunometabolism in the single-cell era[J]. Cell Metab, 2020, 32(5): 710-725.
[22]	Peeters F, Cappuyns S, Piqué-Gili M, et al. Applications of single-cell multi-omics in liver cancer[J]. JHEP Rep, 2024, 6(7): 101094.
[23]	Rigamonti A, Viatore M, Polidori R, et al. Integrating AI-powered digital pathology and imaging mass cytometry identifies key classifiers of tumor cells, stroma, and immune cells in non-small cell lung cancer[J]. Cancer Res, 2024, 84(7): 1165-1177.
[24]	Xie P Y, Guo L, Yu Q, et al. ACE2 enhances sensitivity to PD-L1 blockade by inhibiting macrophage-induced immunosuppression and angiogenesis[J]. Cancer Res, 2025, 85(2): 299-313.
[25]	Brightman S E, Becker A, Thota R R, et al. Neoantigen-specific stem cell memory-like CD4⁺ T cells mediate CD8⁺ T cell-dependent immunotherapy of MHC class Ⅱ-negative solid tumors[J]. Nat Immunol, 2023, 24: 1345-1357.
[26]	Moradpoor R, Salimi M. Crosstalk between tumor cells and immune system leads to epithelial-mesenchymal transition induction and breast cancer progression[J]. Iran Biomed J, 2021, 25(1): 1-7.
[27]	Gulati G S, D'Silva J P, Liu Y H, et al. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics[J]. Nat Rev Mol Cell Biol, 2025, 26(1): 11-31.
[28]	Nolan E, Lindeman G J, Visvader J E. Deciphering breast cancer: from biology to the clinic[J]. Cell, 2023, 186(8): 1708-1728.
[29]	Barras D, Ghisoni E, Chiffelle J, et al. Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8⁺ T-myeloid cell networks in melanoma[J]. Sci Immunol, 2024, 9(92): eadg7995.
[30]	Park J, Hsueh P C, Li Z Y, et al. Microenvironment-driven metabolic adaptations guiding CD8⁺ T cell anti-tumor immunity[J]. Immunity, 2023, 56(1): 32-42.
[31]	van Elsas M J, Middelburg J, Labrie C, et al. Immunotherapy-activated T cells recruit and skew late-stage activated M1-like macrophages that are critical for therapeutic efficacy[J]. Cancer Cell, 2024, 42(6): 1032-1050.e10.
[32]	Nasir I, Mcguinness C, Poh A R, et al. Tumor macrophage functional heterogeneity can inform the development of novel cancer therapies[J]. Trends Immunol, 2023, 44(12): 971-985.
[33]	Beck J D, Diken M, Suchan M, et al. Long-lasting mRNA-encoded interleukin-2 restores CD8⁺ T cell neoantigen immunity in MHC class Ⅰ-deficient cancers[J]. Cancer Cell, 2024, 42(4): 568-582.e11.
[34]	Kersten K, Hu K H, Combes A J, et al. Spatiotemporal co-dependency between macrophages and exhausted CD8⁺ T cells in cancer[J]. Cancer Cell, 2022, 40(6): 624-638.e9.
[35]	Wang K W, Yang Y Q, Wu F J, et al. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data[J]. Nat Commun, 2023, 14(1): 1836.