Single-cell RNA sequencing and its research progress in tumor microenvironment of breast cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Guangwen ¹ , CHENG Chen ¹ ,  WANG Shiming ²

1. The First Clinical College of Shanxi Medical University, Taiyuan 030001, P. R. China;
2. Department of Breast Surgery, The First Hospital of Shanxi Medical University, Taiyuan 030001, P. R. China;

Corresponding author：

WANG Shiming, Email: wangshimingSX@sina.com

Keywords：

single-cell RNA sequencing; breast cancer; tumor microenvironment; tumor heterogeneity

DOI：

10.7507/1007-9424.202311050

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To understand the single-cell RNA sequencing (scRNA-seq) and its research progress in the tumor microenvironment (TME) of breast cancer, in order to provide new ideas and directions for the research and treatment of breast cancer. Method The development of scRNA-seq technology and its related research literature in breast cancer TME at home and abroad in recent years was reviewed. Results The scRNA-seq was a quantum technology in high-throughput sequencing of mRNA at the cellular level, and had become a powerful tool for studying cellular heterogeneity when tissue samples were fewer. While capturing rare cell types, it was expected to accurately describe the complex structure of the TME of breast cancer. Conclusions After decades of development, scRNA-seq has been widely used in tumor research. Breast cancer is a malignant tumor with high heterogeneity. The application of scRNA-seq in breast cancer research can better understand its tumor heterogeneity and TME, and then promote development of personalized diagnosis and treatment.

Citation： ZHANG Guangwen, CHENG Chen, WANG Shiming. Single-cell RNA sequencing and its research progress in tumor microenvironment of breast cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2024, 31(4): 495-501. doi: 10.7507/1007-9424.202311050 Copy

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28.	Su M, Pan T, Chen QZ, et al. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res, 2022, 9(1): 68. doi: 10.1186/s40779-022-00434-8.
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44.	Puram SV, Tirosh I, Parikh AS, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell, 2017, 171(7): 1611-1624.
45.	Valdés-Mora F, Salomon R, Gloss BS, et al. Single-cell transcriptomics reveals involution mimicry during the specification of the basal breast cancer subtype. Cell Rep, 2021, 35(2): 108945. doi: 10.1016/j.celrep.2021.108945.
46.	Kieffer Y, Hocine HR, Gentric G, et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov, 2020, 10(9): 1330-1351.
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54.	Guo S, Liu X, Zhang J, et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer. Comput Biol Med, 2023, 161: 107066. doi: 10.1016/j.compbiomed.2023.107066.
55.	Lu Y, Zhao Q, Liao JY, et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell, 2020, 180(6): 1081-1097.
56.	Hu Q, Hong Y, Qi P, et al. Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling. Nat Commun, 2021, 12(1): 2186. doi: 10.1038/s41467-021-22300-2.
57.	Zilionis R, Engblom C, Pfirschke C, et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity, 2019, 50(5): 1317-1334.
58.	Bao X, Shi R, Zhao T, et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC. Cancer Immunol Immunother, 2021, 70(1): 189-202.
59.	Molgora M, Esaulova E, Vermi W, et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy. Cell, 2020, 182(4): 886-900.
60.	Kersten K, Hu KH, Combes AJ, et al. Spatiotemporal co-dependency between macrophages and exhausted CD8⁺ T cells in cancer. Cancer Cell, 2022, 40(6): 624-638.
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64.	SenGupta S, Hein LE, Xu Y, et al. Triple-negative breast cancer cells recruit neutrophils by secreting TGF-β and CXCR2 ligands. Front Immunol, 2021, 12: 659996. doi: 10.3389/fimmu.2021.659996.
65.	Amer HT, Stein U, El Tayebi HM. The monocyte, a maestro in the tumor microenvironment (TME) of breast cancer. Cancers (Basel), 2022, 14(21): 5460. doi: 10.3390/cancers14215460.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Olsen TK, Baryawno N. Introduction to single-cell RNA sequencing. Curr Protoc Mol Biol, 2018, 122(1): e57. doi: 10.1002/cpmb.57.
3. Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol, 2018, 14(8): 479-492.
4. Lei Y, Tang R, Xu J, et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol, 2021, 14(1): 91. doi: 10.1186/s13045-021-01105-2.
5. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-trans-criptome analysis of a single cell. Nat Methods, 2009, 6(5): 377-382.
6. Wang X, He Y, Zhang Q, et al. Direct comparative analyses of 10×Genomics chromium and Smart-seq2. Genomics Proteomics Bioinformatics, 2021, 19(2): 253-266.
7. Wang S, Sun ST, Zhang XY, et al. The evolution of single-cell RNA sequencing technology and application: progress and perspectives. Int J Mol Sci, 2023, 24(3): 2943. doi: 10.3390/ijms24032943.
8. Wang W, Zhong Y, Zhuang Z, et al. Multiregion single-cell sequencing reveals the transcriptional landscape of the immune microenvironment of colorectal cancer. Clin Transl Med, 2021, 11(1): e253. doi: 10.1002/ctm2.253.
9. See P, Lum J, Chen J, et al. A single-cell sequencing guide for immunologists. Front Immunol, 2018, 9: 2425. doi: 10.3389/fimmu.2018.02425.
10. Williams CG, Lee HJ, Asatsuma T, et al. An introduction to spatial transcriptomics for biomedical research. Genome Med, 2022, 14(1): 68. doi: 10.1186/s13073-022-01075-1.
11. Zhang Y, Wang D, Peng M, et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res, 2021, 40(1): 81. doi: 10.1186/s13046-021-01874-1.
12. Jovic D, Liang X, Zeng H, et al. Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med, 2022, 12(3): e694. doi: 10.1002/ctm2.694.
13. Kaushik AM, Hsieh K, Wang TH. Droplet microfluidics for high-sensitivity and high-throughput detection and screening of disease biomarkers. Wiley Interdiscip Rev Nanomed Nanobiotechnol, 2018, 10(6): e1522. doi: 10.1002/wnan.1522.
14. Lindsay CR, Blackhall FH, Carmel A, et al. EPAC-lung: pooled analysis of circulating tumour cells in advanced non-small cell lung cancer. Eur J Cancer, 2019, 117: 60-68.
15. 李贱成, 徐克前. 单细胞转录组测序技术及其应用. 生命的化学, 2020, 40(8): 1208-1219.
16. Zheng GX, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun, 2017, 8: 14049. doi: 10.1038/ncomms14049.
17. Balzer MS, Ma Z, Zhou J, et al. How to get started with single cell RNA sequencing data analysis. J Am Soc Nephrol, 2021, 32(6): 1279-1292.
18. Griffiths JA, Richard AC, Bach K, et al. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat Commun, 2018, 9(1): 2667. doi: 10.1038/s41467-018-05083-x.
19. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol, 2019, 20(1): 296. doi: 10.1186/s13059-019-1874-1.
20. Bacher R, Chu LF, Leng N, et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods, 2017, 14(6): 584-586.
21. Tang W, Bertaux F, Thomas P, et al. BayNorm: bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics, 2020, 36(4): 1174-1181.
22. Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol, 2019, 37(6): 685-691.
23. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods, 2019, 16(12): 1289-1296.
24. Zhou Y, Sharpee TO. Using global t-SNE to preserve intercluster data structure. Neural Comput, 2022, 34(8): 1637-1651.
25. Armstrong G, Martino C, Rahman G, et al. Uniform manifold approximation and projection (UMAP) reveals composite patterns and resolves visualization artifacts in microbiome data. mSystems, 2021, 6(5): e0069121. doi: 10.1128/mSystems.00691-21.
26. Coifman RR, Lafon S, Lee AB, et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci USA, 2005, 102(21): 7426-7431.
27. 李涛, 张泽坤, 顾连峰. 高通量单细胞转录组数据分析方法的研究进展. 福建农林大学学报(自然科学版), 2022, 51(2): 145-154.
28. Su M, Pan T, Chen QZ, et al. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res, 2022, 9(1): 68. doi: 10.1186/s40779-022-00434-8.
29. Welch DR. Tumor heterogeneity—A‘Contemporary Concept’ founded on historical insights and predictions. Cancer Res, 2016, 76(1): 4-6.
30. Liedtke C, Mazouni C, Hess KR, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol, 2023, 41(10): 1809-1815.
31. Karaayvaz M, Cristea S, Gillespie SM, et al. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat Commun, 2018, 9(1): 3588. doi: 10.1038/s41467-018-06052-0.
32. Hida K, Maishi N, Annan DA, et al. Contribution of tumor endothelial cells in cancer progression. Int J Mol Sci, 2018, 19(5): 1272. doi: 10.3390/ijms19051272.
33. Zhang J, Lu T, Lu S, et al. Single-cell analysis of multiple cancer types reveals differences in endothelial cells between tumors and normal tissues. Comput Struct Biotechnol J, 2022, 21: 665-676.
34. El-Deiry WS, Goldberg RM, Lenz HJ, et al. The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA Cancer J Clin, 2019, 69(4): 305-343.
35. Hamilton E, Shastry M, Shiller SM, et al. Targeting HER2 heterogeneity in breast cancer. Cancer Treat Rev, 2021, 100: 102286. doi: 10.1016/j.ctrv.2021.102286.
36. 中国抗癌协会乳腺癌专业委员会. 中国抗癌协会乳腺癌诊治指南与规范(2021年版). 中国癌症杂志, 2021, 31(10): 954-1040.
37. Chung W, Eum HH, Lee HO, et al. Single-cell RNA-seq enables com-prehensive tumour and immune cell profiling in primary breast cancer. Nat Commun, 2017, 8: 15081. doi: 10.1038/ncomms15081.
38. Wang Q, Guldner IH, Golomb SM, et al. Single-cell profiling guided combinatorial immunotherapy for fast-evolving CDK4/6 inhibitor-resistant HER2-positive breast cancer. Nat Commun, 2019, 10(1): 3817. doi: 10.1038/s41467-019-11729-1.
39. Jang BS, Han W, Kim IA. Tumor mutation burden, immune checkpoint crosstalk and radiosensitivity in single-cell RNA sequencing data of breast cancer. Radiother Oncol, 2020, 142: 202-209.
40. Zhou S, Huang YE, Liu H, et al. Single-cell RNA-seq dissects the intra-tumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks. Mol Ther Nucleic Acids, 2021, 23: 682-690.
41. Mao X, Xu J, Wang W, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer, 2021, 20(1): 131. doi: 10.1186/s12943-021-01428-1.
42. Ermakov MS, Nushtaeva AA, Richter VA, et al. Cancer-associated fibroblasts and their role in tumor progression. Vavilovskii Zhurnal Genet Selektsii, 2022, 26(1): 14-21.
43. Xiang X, Niu YR, Wang ZH, et al. Cancer-associated fibroblasts: vital suppressors of the immune response in the tumor microenvironment. Cytokine Growth Factor Rev, 2022, 67: 35-48.
44. Puram SV, Tirosh I, Parikh AS, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell, 2017, 171(7): 1611-1624.
45. Valdés-Mora F, Salomon R, Gloss BS, et al. Single-cell transcriptomics reveals involution mimicry during the specification of the basal breast cancer subtype. Cell Rep, 2021, 35(2): 108945. doi: 10.1016/j.celrep.2021.108945.
46. Kieffer Y, Hocine HR, Gentric G, et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov, 2020, 10(9): 1330-1351.
47. Cords L, Tietscher S, Anzeneder T, et al. Cancer-associated fibroblast classification in single-cell and spatial proteomics data. Nat Commun, 2023, 14(1): 4294. doi: 10.1038/s41467-023-39762-1.
48. Li C, Yang L, Zhang Y, et al. Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer. Front Immunol, 2024, 14: 1307588. doi: 10.3389/fimmu.2023.1307588.
49. Hiam-Galvez KJ, Allen BM, Spitzer MH. Systemic immunity in cancer. Nat Rev Cancer, 2021, 21(6): 345-359.
50. Xu Q, Chen S, Hu Y, et al. Landscape of immune microenviron-ment under immune cell infiltration pattern in breast cancer. Front Immunol, 2021, 12: 711433. doi: 10.3389/fimmu.2021.711433.
51. Pal B, Chen Y, Vaillant F, et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J, 2021, 40(11): e107333. doi: 10.15252/embj.2020107333.
52. Azizi E, Carr AJ, Plitas G, et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell, 2018, 174(5): 1293-1308.
53. Savas P, Virassamy B, Ye C, et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med, 2018, 24(7): 986-993.
54. Guo S, Liu X, Zhang J, et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer. Comput Biol Med, 2023, 161: 107066. doi: 10.1016/j.compbiomed.2023.107066.
55. Lu Y, Zhao Q, Liao JY, et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell, 2020, 180(6): 1081-1097.
56. Hu Q, Hong Y, Qi P, et al. Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling. Nat Commun, 2021, 12(1): 2186. doi: 10.1038/s41467-021-22300-2.
57. Zilionis R, Engblom C, Pfirschke C, et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity, 2019, 50(5): 1317-1334.
58. Bao X, Shi R, Zhao T, et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC. Cancer Immunol Immunother, 2021, 70(1): 189-202.
59. Molgora M, Esaulova E, Vermi W, et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy. Cell, 2020, 182(4): 886-900.
60. Kersten K, Hu KH, Combes AJ, et al. Spatiotemporal co-dependency between macrophages and exhausted CD8⁺ T cells in cancer. Cancer Cell, 2022, 40(6): 624-638.
61. Wu SZ, Al-Eryani G, Roden DL, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet, 2021, 53(9): 1334-1347.
62. Huang H, Zhang H, Onuma AE, et al. Neutrophil elastase and neutrophil extracellular traps in the tumor microenvironment. Adv Exp Med Biol, 2020, 1263: 13-23.
63. Jaillon S, Ponzetta A, Di Mitri D, et al. Neutrophil diversity and plasticity in tumour progression and therapy. Nat Rev Cancer, 2020, 20(9): 485-503.
64. SenGupta S, Hein LE, Xu Y, et al. Triple-negative breast cancer cells recruit neutrophils by secreting TGF-β and CXCR2 ligands. Front Immunol, 2021, 12: 659996. doi: 10.3389/fimmu.2021.659996.
65. Amer HT, Stein U, El Tayebi HM. The monocyte, a maestro in the tumor microenvironment (TME) of breast cancer. Cancers (Basel), 2022, 14(21): 5460. doi: 10.3390/cancers14215460.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Single-cell RNA sequencing and its research progress in tumor microenvironment of breast cancer

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