Top

Journal of Experimental & Clinical Cancer Research

Published in:

Open Access 01-12-2021 | Metastasis | Review

Single‐cell RNA sequencing in cancer research

Authors: Yijie Zhang, Dan Wang, Miao Peng, Le Tang, Jiawei Ouyang, Fang Xiong, Can Guo, Yanyan Tang, Yujuan Zhou, Qianjin Liao, Xu Wu, Hui Wang, Jianjun Yu, Yong Li, Xiaoling Li, Guiyuan Li, Zhaoyang Zeng, Yixin Tan, Wei Xiong

Published in: Journal of Experimental & Clinical Cancer Research | Issue 1/2021

Abstract

Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.

Wu C, Li M, Meng H, Liu Y, Niu W, Zhou Y, et al. Analysis of status and countermeasures of cancer incidence and mortality in China. Sci China Life Sci. 2019;62:640–47. https://doi.org/10.1007/s11427-018-9461-5.

Xiao L, Wei F, Liang F, Li Q, Deng H, Tan S, et al. TSC22D2 identified as a candidate susceptibility gene of multi-cancer pedigree using genome-wide linkage analysis and whole-exome sequencing. Carcinogenesis. 2019;40:819–27. https://doi.org/10.1093/carcin/bgz095.

Fan C, Tu C, Qi P, Guo C, Xiang B, Zhou M, et al. GPC6 Promotes Cell Proliferation, Migration, and Invasion in Nasopharyngeal Carcinoma. J Cancer. 2019;10:3926–32. https://doi.org/10.7150/jca.31345.CrossRefPubMedPubMedCentral

Tu C, Zeng Z, Qi P, Li X, Guo C, Xiong F, et al. Identification of genomic alterations in nasopharyngeal carcinoma and nasopharyngeal carcinoma-derived Epstein-Barr virus by whole-genome sequencing. Carcinogenesis. 2018;39:1517–28. https://doi.org/10.1093/carcin/bgy108.CrossRefPubMed

Ge J, Wang J, Wang H, Jiang X, Liao Q, Gong Q, et al. The BRAF V600E mutation is a predictor of the effect of radioiodine therapy in papillary thyroid cancer. J Cancer. 2020;11:932–39. https://doi.org/10.7150/jca.33105.CrossRefPubMedPubMedCentral

Swanton C. Cancer evolution constrained by mutation order. N Engl J Med. 2015;372:661–3. https://doi.org/10.1056/NEJMe1414288.CrossRefPubMed

Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med. 2017;376:2109–21. https://doi.org/10.1056/NEJMoa1616288.CrossRefPubMed

Hu Z, Sun R, Curtis C. A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochim Biophys Acta Rev Cancer. 2017;1867:109–26. https://doi.org/10.1016/j.bbcan.2017.03.001.

Wei F, Wang D, Wei J, Tang N, Tang L, Xiong F, et al. Metabolic crosstalk in the tumor microenvironment regulates antitumor immunosuppression and immunotherapy resisitance. Cell Mol Life Sci. 2021;78:173–93. https://doi.org/10.1007/s00018-020-03581-0.CrossRefPubMed

10.

Fan C, Zhang S, Gong Z, Li X, Xiang B, Deng H, et al. Emerging role of metabolic reprogramming in tumor immune evasion and immunotherapy. Sci China Life Sci. 2020. https://doi.org/10.1007/s11427-019-1735-4.

11.

Saunders NA, Simpson F, Thompson EW, Hill MM, Endo-Munoz L, Leggatt G, et al. Role of intratumoural heterogeneity in cancer drug resistance: molecular and clinical perspectives. EMBO Mol Med. 2012;4:675–84. https://doi.org/10.1002/emmm.201101131.

12.

Wei X, Chen Y, Jiang X, Peng M, Liu Y, Mo Y, et al. Mechanisms of vasculogenic mimicry in hypoxic tumor microenvironments. Mol Cancer. 2021;20:7. https://doi.org/10.1186/s12943-020-01288-1.CrossRefPubMedPubMedCentral

13.

Lee HO, Silva AS, Concilio S, Li YS, Slifker M, Gatenby RA, et al. Evolution of tumor invasiveness: the adaptive tumor microenvironment landscape model. Cancer Res. 2011;71:6327–37. https://doi.org/10.1158/0008-5472.Can-11-0304.

14.

Lloyd MC, Cunningham JJ, Bui MM, Gillies RJ, Brown JS, Gatenby RA. Darwinian Dynamics of Intratumoral Heterogeneity: Not Solely Random Mutations but Also Variable Environmental Selection Forces. Cancer Res. 2016;76:3136–44. https://doi.org/10.1158/0008-5472.Can-15-2962.

15.

Jiang X, Wang J, Deng X, Xiong F, Zhang S, Gong Z, et al. The role of microenvironment in tumor angiogenesis. J Exp Clin Cancer Res. 2020;39:204. https://doi.org/10.1186/s13046-020-01709-5.

16.

Losic B, Craig AJ, Villacorta-Martin C, Martins-Filho SN, Akers N, Chen X, et al. Intratumoral heterogeneity and clonal evolution in liver cancer. Nat Commun. 2020;11:291. https://doi.org/10.1038/s41467-019-14050-z.

17.

Reuben A, Zhang J, Chiou SH, Gittelman RM, Li J, Lee WC, et al. Comprehensive T cell repertoire characterization of non-small cell lung cancer. Nat Commun. 2020;11:603. https://doi.org/10.1038/s41467-019-14273-0.

18.

Zhang C, Ding H, Huang H, Palashati H, Miao Y, Xiong H, et al. TCR repertoire intratumor heterogeneity of CD4(+) and CD8(+) T cells in centers and margins of localized lung adenocarcinomas. Int J Cancer. 2019;144:818–27. https://doi.org/10.1002/ijc.31760.

19.

Zhu K, Li P, Mo Y, Wang J, Jiang X, Ge J, et al. Neutrophils: Accomplices in metastasis. Cancer Lett. 2020;492:11–20. https://doi.org/10.1016/j.canlet.2020.07.028.

20.

Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014;346:256–9. https://doi.org/10.1126/science.1256930.CrossRefPubMedPubMedCentral

21.

Chen YP, Zhang Y, Lv JW, Li YQ, Wang YQ, He QM, et al. Genomic Analysis of Tumor Microenvironment Immune Types across 14 Solid Cancer Types: Immunotherapeutic Implications. Theranostics. 2017;7:3585–94. https://doi.org/10.7150/thno.21471.CrossRefPubMedPubMedCentral

22.

Alvarez-Arenas A, Podolski-Renic A, Belmonte-Beitia J, Pesic M, Calvo GF. Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer. Sci Rep. 2019;9:9332. https://doi.org/10.1038/s41598-019-45863-z.

23.

Duan S, Guo W, Xu Z, He Y, Liang C, Mo Y, et al. Natural killer group 2D receptor and its ligands in cancer immune escape. Mol Cancer. 2019;18:29. https://doi.org/10.1186/s12943-019-0956-8.CrossRefPubMedPubMedCentral

24.

Ren D, Hua Y, Yu B, Ye X, He Z, Li C, et al. Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy. Mol Cancer. 2020;19:19. https://doi.org/10.1186/s12943-020-1144-6.CrossRefPubMedPubMedCentral

25.

Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008;453:544–7. https://doi.org/10.1038/nature06965.CrossRefPubMedPubMedCentral

26.

Alves JM, Posada D. Sensitivity to sequencing depth in single-cell cancer genomics. Genome Med. 2018;10:29. https://doi.org/10.1186/s13073-018-0537-2.

27.

McGranahan N, Swanton C. Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell. 2017;168:613–28. https://doi.org/10.1016/j.cell.2017.01.018.CrossRefPubMed

28.

Navin NE. Delineating cancer evolution with single-cell sequencing. Sci Transl Med. 2015;7:296fs29. https://doi.org/10.1126/scitranslmed.aac8319.

29.

Lahnemann D, Koster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21:31. https://doi.org/10.1186/s13059-020-1926-6.

30.

Kuipers J, Jahn K, Beerenwinkel N. Advances in understanding tumour evolution through single-cell sequencing. Biochim Biophys Acta Rev Cancer. 2017;1867:127–38. https://doi.org/10.1016/j.bbcan.2017.02.001.

31.

McGinnis CS, Patterson DM, Winkler J, Conrad DN, Hein MY, Srivastava V, et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat Methods. 2019;16:619–26. https://doi.org/10.1038/s41592-019-0433-8.

32.

Peng M, Mo Y, Wang Y, Wu P, Zhang Y, Xiong F, et al. Neoantigen vaccine: an emerging tumor immunotherapy. Mol Cancer. 2019;18:128. https://doi.org/10.1186/s12943-019-1055-6.CrossRefPubMedPubMedCentral

33.

Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50:96. https://doi.org/10.1038/s12276-018-0071-8.CrossRefPubMedPubMedCentral

34.

Hedlund E, Deng Q. Single-cell RNA sequencing: Technical advancements and biological applications. Mol Aspects Med. 2018;59:36–46. https://doi.org/10.1016/j.mam.2017.07.003.CrossRefPubMed

35.

Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P. Technologies for Single-Cell Isolation. Int J Mol Sci. 2015;16:16897–919. https://doi.org/10.3390/ijms160816897.CrossRefPubMedPubMedCentral

36.

Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol. 2018;14:479–92. https://doi.org/10.1038/s41581-018-0021-7.

37.

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82. https://doi.org/10.1038/nmeth.1315.

38.

Kang HM, Subramaniam M, Targ S, Nguyen M, Maliskova L, McCarthy E, et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol. 2018;36:89–94. https://doi.org/10.1038/nbt.4042.

39.

Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015;161:1202–14. https://doi.org/10.1016/j.cell.2015.05.002.CrossRefPubMedPubMedCentral

40.

Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc. 2010;5:516–35. https://doi.org/10.1038/nprot.2009.236.

41.

Picelli S. Single-cell RNA-sequencing: The future of genome biology is now. RNA Biol. 2017;14:637–50. https://doi.org/10.1080/15476286.2016.1201618.CrossRefPubMed

42.

Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73. https://doi.org/10.1016/j.celrep.2012.08.003.

43.

Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-SEq. Genome Biol. 2016;17:77. https://doi.org/10.1186/s13059-016-0938-8.

44.

Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82. https://doi.org/10.1038/nbt.2282.

45.

Goetz JJ, Trimarchi JM. Transcriptome sequencing of single cells with Smart-SEq. Nat Biotechnol. 2012;30:763–5. https://doi.org/10.1038/nbt.2325.

46.

Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8. https://doi.org/10.1038/nmeth.2639.

47.

Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014;9:171–81. https://doi.org/10.1038/nprot.2014.006.

48.

Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-sEq. Genome Res. 2011;21:1160–7. https://doi.org/10.1101/gr.110882.110.

49.

Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58:610–20. https://doi.org/10.1016/j.molcel.2015.04.005.

50.

Bhargava V, Head SR, Ordoukhanian P, Mercola M, Subramaniam S. Technical variations in low-input RNA-seq methodologies. Sci Rep. 2014;4:3678. https://doi.org/10.1038/srep03678.

51.

Natarajan KN. Single-Cell Tagged Reverse Transcription (STRT-Seq). Methods Mol Biol. 2019;1979:133 – 53. https://doi.org/10.1007/978-1-4939-9240-9_9.

52.

Kiselev VY, Andrews TS, Hemberg M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet. 2019;20:273–82. https://doi.org/10.1038/s41576-018-0088-9.

53.

Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495–502. https://doi.org/10.1038/nbt.3192.

54.

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–86. https://doi.org/10.1038/nbt.2859.

55.

Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19:15. https://doi.org/10.1186/s13059-017-1382-0.

56.

Yip SH, Wang P, Kocher JA, Sham PC, Wang J. Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res. 2017;45:e179. https://doi.org/10.1093/nar/gkx828.

57.

Luo G, Gao Q, Zhang S, Yan B. Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives. Comput Struct Biotechnol J. 2020;18:2962–71. https://doi.org/10.1016/j.csbj.2020.10.016.

58.

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14:865–68. https://doi.org/10.1038/nmeth.4380.

59.

Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P, Klughammer J, et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods. 2017;14:297–301. https://doi.org/10.1038/nmeth.4177.

60.

Hill AJ, McFaline-Figueroa JL, Starita LM, Gasperini MJ, Matreyek KA, Packer J, et al. On the design of CRISPR-based single-cell molecular screens. Nat Methods. 2018;15:271–74. https://doi.org/10.1038/nmeth.4604.

61.

McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer cell. 2015;27:15–26. https://doi.org/10.1016/j.ccell.2014.12.001.CrossRefPubMed

62.

Blanpain C. Tracing the cellular origin of cancer. Nat Cell Biol. 2013;15:126–34. https://doi.org/10.1038/ncb2657.CrossRefPubMed

63.

Lourenco AR, Ban Y, Crowley MJ, Lee SB, Ramchandani D, Du W, et al. Differential Contributions of Pre- and Post-EMT Tumor Cells in Breast Cancer Metastasis. Cancer Res. 2020;80:163–69. https://doi.org/10.1158/0008-5472.Can-19-1427.

64.

Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA, Phung AT, et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol. 2020;22:310–20. https://doi.org/10.1038/s41556-020-0477-0.CrossRefPubMed

65.

Müller M, Bird TG, Nault JC. The landscape of gene mutations in cirrhosis and hepatocellular carcinoma. J Hepatol. 2020;72:990–1002. https://doi.org/10.1016/j.jhep.2020.01.019.

66.

Rebouissou S, Nault JC. Advances in molecular classification and precision oncology in hepatocellular carcinoma. J Hepatol. 2020;72:215–29. https://doi.org/10.1016/j.jhep.2019.08.017.

67.

Watson IR, Takahashi K, Futreal PA, Chin L. Emerging patterns of somatic mutations in cancer. Nat Rev Genet. 2013;14:703–18. https://doi.org/10.1038/nrg3539.

68.

Baslan T, Hicks J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer. 2017;17:557–69. https://doi.org/10.1038/nrc.2017.58.

69.

Hosein AN, Huang H, Wang Z, Parmar K, Du W, Huang J, et al. Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution. JCI insight. 2019;5:https://doi.org/10.1172/jci.insight.129212.

70.

Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016;352:169–75. https://doi.org/10.1126/science.aaf2784.CrossRefPubMed

71.

Hunter KW, Amin R, Deasy S, Ha NH, Wakefield L. Genetic insights into the morass of metastatic heterogeneity. Nat Rev Cancer. 2018;18:211–23. https://doi.org/10.1038/nrc.2017.126.

72.

Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet. 2003;33:49–54. https://doi.org/10.1038/ng1060.CrossRefPubMed

73.

Chen YC, Sahoo S, Brien R, Jung S, Humphries B, Lee W, et al. Single-cell RNA-sequencing of migratory breast cancer cells: discovering genes associated with cancer metastasis. Analyst. 2019;144:7296–309. https://doi.org/10.1039/c9an01358j.CrossRefPubMed

74.

Kim KT, Lee HW, Lee HO, Song HJ, Jeong da E, Shin S, et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 2016;17:80. https://doi.org/10.1186/s13059-016-0945-9.

75.

Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell. 2019;178:835–49. https://doi.org/10.1016/j.cell.2019.06.024. :.e21.CrossRefPubMedPubMedCentral

76.

van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, et al. Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Cell. 2019;176:1265–81. https://doi.org/10.1016/j.cell.2019.01.031.

77.

Flavahan WA, Gaskell E, Bernstein BE. Epigenetic plasticity and the hallmarks of cancer. Science. 2017;357:https://doi.org/10.1126/science.aal2380.

78.

Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486–90. https://doi.org/10.1038/nature14590.CrossRefPubMedPubMedCentral

79.

Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4. https://doi.org/10.1126/science.aab1601.CrossRefPubMedPubMedCentral

80.

Clark SJ, Argelaguet R, Kapourani CA, Stubbs TM, Lee HJ, Alda-Catalinas C, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun. 2018;9:781. https://doi.org/10.1038/s41467-018-03149-4.

81.

Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature. 2013;501:328–37. https://doi.org/10.1038/nature12624.CrossRefPubMedPubMedCentral

82.

Shackleton M, Quintana E, Fearon ER, Morrison SJ. Heterogeneity in cancer: cancer stem cells versus clonal evolution. Cell. 2009;138:822–9. https://doi.org/10.1016/j.cell.2009.08.017.CrossRefPubMed

83.

Mo Y, Wang Y, Zhang L, Yang L, Zhou M, Li X, et al. The role of Wnt signaling pathway in tumor metabolic reprogramming. J Cancer. 2019;10:3789–97. https://doi.org/10.7150/jca.31166.CrossRefPubMedPubMedCentral

84.

Fendler A, Bauer D, Busch J, Jung K, Wulf-Goldenberg A, Kunz S, et al. Inhibiting WNT and NOTCH in renal cancer stem cells and the implications for human patients. Nat Commun. 2020;11:929. https://doi.org/10.1038/s41467-020-14700-7.

85.

Pan XW, Zhang H, Xu D, Chen JX, Chen WJ, Gan SS, et al. Identification of a novel cancer stem cell subpopulation that promotes progression of human fatal renal cell carcinoma by single-cell RNA-seq analysis. Int J Biol Sci. 2020;16:3149–62. https://doi.org/10.7150/ijbs.46645.CrossRefPubMedPubMedCentral

86.

Nam AS, Kim KT, Chaligne R, Izzo F, Ang C, Taylor J, et al. Somatic mutations and cell identity linked by Genotyping of Transcriptomes. Nature. 2019;571:355–60. https://doi.org/10.1038/s41586-019-1367-0.CrossRefPubMedPubMedCentral

87.

Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782–95. https://doi.org/10.1016/j.immuni.2013.10.003.CrossRefPubMed

88.

Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, et al. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Res. 2011;71:1263–71. https://doi.org/10.1158/0008-5472.Can-10-2907.

89.

Talmadge JE, Donkor M, Scholar E. Inflammatory cell infiltration of tumors: Jekyll or Hyde. Cancer Metastasis Rev. 2007;26:373–400. https://doi.org/10.1007/s10555-007-9072-0.CrossRefPubMed

90.

Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12:298–306. https://doi.org/10.1038/nrc3245.

91.

Shembrey C, Huntington ND, Hollande F. Impact of Tumor and Immunological Heterogeneity on the Anti-Cancer Immune Response. Cancers (Basel). 2019;11:https://doi.org/10.3390/cancers11091217.

92.

Zhao J, Guo C, Xiong F, Yu J, Ge J, Wang H, et al. Single cell RNA-seq reveals the landscape of tumor and infiltrating immune cells in nasopharyngeal carcinoma. Cancer Lett. 2020;477:131–43. https://doi.org/10.1016/j.canlet.2020.02.010.

93.

Zhang YL, Li J, Mo HY, Qiu F, Zheng LM, Qian CN, et al. Different subsets of tumor infiltrating lymphocytes correlate with NPC progression in different ways. Mol Cancer. 2010;9:4. https://doi.org/10.1186/1476-4598-9-4.CrossRefPubMedPubMedCentral

94.

Chung W, Eum HH, Lee HO, Lee KM, Lee HB, Kim KT, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8:15081. https://doi.org/10.1038/ncomms15081.

95.

Proserpio V, Mahata B. Single-cell technologies to study the immune system. Immunology. 2016;147:133–40. https://doi.org/10.1111/imm.12553.CrossRefPubMed

96.

Park JE, Botting RA, Dominguez Conde C, Popescu DM, Lavaert M, Kunz DJ, et al. A cell atlas of human thymic development defines T cell repertoire formatio. Science (New York, NY). 2020;367. https://doi.org/10.1126/science.aay3224.

97.

Chen Z, Ji Z, Ngiow SF, Manne S, Cai Z, Huang AC, et al. TCF-1-Centered Transcriptional Network Drives an Effector versus Exhausted CD8 T Cell-Fate Decision. Immunity. 2019;51:840–55.e5. https://doi.org/10.1016/j.immuni.2019.09.013.

98.

Zhang L, Yu X, Zheng L, Zhang Y, Li Y, Fang Q, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018;564:268–72. https://doi.org/10.1038/s41586-018-0694-x.CrossRefPubMed

99.

Pan Y, Lu F, Fei Q, Yu X, Xiong P, Yu X, et al. Single-cell RNA sequencing reveals compartmental remodeling of tumor-infiltrating immune cells induced by anti-CD47 targeting in pancreatic cancer. J Hematol Oncol. 2019;12:124. https://doi.org/10.1186/s13045-019-0822-6.CrossRefPubMedPubMedCentral

100.

Shankaran V, Ikeda H, Bruce AT, White JM, Swanson PE, Old LJ, et al. IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature. 2001;410:1107–11. https://doi.org/10.1038/35074122.CrossRefPubMed

101.

Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol. 2002;3:991–8. https://doi.org/10.1038/ni1102-991.

102.

Dunn GP, Old LJ, Schreiber RD. The three Es of cancer immunoediting. Annu Rev Immunol. 2004;22:329–60. https://doi.org/10.1146/annurev.immunol.22.012703.104803.CrossRefPubMed

103.

Wang B, Zhang W, Jankovic V, Golubov J, Poon P, Oswald EM, et al. Combination cancer immunotherapy targeting PD-1 and GITR can rescue CD8(+) T cell dysfunction and maintain memory phenotype. Sci Immunol. 2018;3:https://doi.org/10.1126/sciimmunol.aat7061.

104.

Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54. https://doi.org/10.1056/NEJMoa1200690.CrossRefPubMedPubMedCentral

105.

Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012;366:2455–65. https://doi.org/10.1056/NEJMoa1200694.CrossRefPubMedPubMedCentral

106.

Jiang X, Wang J, Deng X, Xiong F, Ge J, Xiang B, et al. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer. 2019;18:10. https://doi.org/10.1186/s12943-018-0928-4.CrossRefPubMedPubMedCentral

107.

Xiong F, Deng S, Huang HB, Li XY, Zhang WL, Liao QJ, et al. Effects and mechanisms of innate immune molecules on inhibiting nasopharyngeal carcinoma. Chin Med J. 2019;132:749–52. https://doi.org/10.1097/cm9.0000000000000132.CrossRefPubMedPubMedCentral

108.

Andrews LP, Marciscano AE, Drake CG, Vignali DA. LAG3 (CD223) as a cancer immunotherapy target. Immunol Rev. 2017;276:80–96. https://doi.org/10.1111/imr.12519.

109.

Monney L, Sabatos CA, Gaglia JL, Ryu A, Waldner H, Chernova T, et al. Th1-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature. 2002;415:536–41. https://doi.org/10.1038/415536a.CrossRefPubMed

110.

Lines JL, Pantazi E, Mak J, Sempere LF, Wang L, O’Connell S, et al. VISTA is an immune checkpoint molecule for human T cells. Cancer Res. 2014;74:1924–32. https://doi.org/10.1158/0008-5472.Can-13-1504.

111.

Chapoval AI, Ni J, Lau JS, Wilcox RA, Flies DB, Liu D, et al. B7-H3: a costimulatory molecule for T cell activation and IFN-gamma production. Nat Immunol. 2001;2:269–74. https://doi.org/10.1038/85339.

112.

Li B, Severson E, Pignon JC, Zhao H, Li T, Novak J, et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 2016;17:174. https://doi.org/10.1186/s13059-016-1028-7.

113.

Chattopadhyay PK, Roederer M, Bolton DL. A deadly dance: the choreography of host-pathogen interactions, as revealed by single-cell technologies. Nat Commun. 2018;9:4638. https://doi.org/10.1038/s41467-018-06214-0.

114.

Andor N, Simonds EF, Czerwinski DK, Chen J, Grimes SM, Wood-Bouwens C, et al. Single-cell RNA-Seq of follicular lymphoma reveals malignant B-cell types and coexpression of T-cell immune checkpoints. Blood. 2019;133:1119–29. https://doi.org/10.1182/blood-2018-08-862292.CrossRefPubMedPubMedCentral

115.

Goswami S, Walle T, Cornish AE, Basu S, Anandhan S, Fernandez I, et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat Med. 2020;26:39–46. https://doi.org/10.1038/s41591-019-0694-x.

116.

Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature. 2014;515:577–81. https://doi.org/10.1038/nature13988.CrossRefPubMedPubMedCentral

117.

Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348:69–74. https://doi.org/10.1126/science.aaa4971.CrossRefPubMed

118.

Gubin MM, Artyomov MN, Mardis ER, Schreiber RD. Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Investig. 2015;125:3413–21. https://doi.org/10.1172/jci80008.CrossRefPubMed

119.

Kosaloglu-Yalcin Z, Lanka M, Frentzen A, Logandha Ramamoorthy Premlal A, Sidney J, Vaughan K, et al. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology. 2018;7:e1492508. https://doi.org/10.1080/2162402x.2018.1492508.CrossRefPubMedPubMedCentral

120.

Nonomura C, Otsuka M, Kondou R, Iizuka A, Miyata H, Ashizawa T, et al. Identification of a neoantigen epitope in a melanoma patient with good response to anti-PD-1 antibody therapy. Immunol Lett. 2019;208:52–9. https://doi.org/10.1016/j.imlet.2019.02.004.

121.

Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, et al. High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy. Cell. 2018;175:1014–30. https://doi.org/10.1016/j.cell.2018.09.030.

122.

Chowell D, Morris LGT, Grigg CM, Weber JK, Samstein RM, Makarov V, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science. 2018;359:582–87. https://doi.org/10.1126/science.aao4572.CrossRefPubMed

123.

McGranahan N, Rosenthal R, Hiley CT, Rowan AJ, Watkins TBK, Wilson GA, et al. Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell. 2017;171:1259–71.e11. https://doi.org/10.1016/j.cell.2017.10.001.CrossRefPubMedPubMedCentral

124.

Paulson KG, Voillet V, McAfee MS, Hunter DS, Wagener FD, Perdicchio M, et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat Commun. 2018;9:3868. https://doi.org/10.1038/s41467-018-06300-3.

125.

Calbo J, van Montfort E, Proost N, van Drunen E, Beverloo HB, Meuwissen R, et al. A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer. Cancer cell. 2011;19:244–56. https://doi.org/10.1016/j.ccr.2010.12.021.CrossRefPubMed

126.

Song Q, Hawkins GA, Wudel L, Chou PC, Forbes E, Pullikuth AK, et al. Dissecting intratumoral myeloid cell plasticity by single cell RNA-sEq. Cancer Med. 2019;8:3072–85. https://doi.org/10.1002/cam4.2113.

127.

Bartoschek M, Oskolkov N, Bocci M, Lövrot J, Larsson C, Sommarin M, et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nat Commun. 2018;9:5150. https://doi.org/10.1038/s41467-018-07582-3.

128.

Zhou JX, Taramelli R, Pedrini E, Knijnenburg T, Huang S. Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes. Sci Rep. 2017;7:8815. https://doi.org/10.1038/s41598-017-09307-w.

129.

Yuan D, Tao Y, Chen G, Shi T. Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma. Cell Commun Signal. 2019;17:48. https://doi.org/10.1186/s12964-019-0363-1.

130.

Kumar MP, Du J, Lagoudas G, Jiao Y, Sawyer A, Drummond DC, et al. Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics. Cell Rep. 2018;25:1458–68. https://doi.org/10.1016/j.celrep.2018.10.047.

131.

Rozenblatt-Rosen O, Regev A, Oberdoerffer P, Nawy T, Hupalowska A, Rood JE, et al. The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell. 2020;181:236–49. https://doi.org/10.1016/j.cell.2020.03.053.CrossRefPubMed

132.

Tang L, Xiong W, Zhang L, Wang D, Wang Y, Wu Y, et al. circSETD3 regulates MAPRE1 through miR-615-5p and miR-1538 sponges to promote migration and invasion in nasopharyngeal carcinoma. Oncogene. 2021;40:307–21. https://doi.org/10.1038/s41388-020-01531-5.CrossRefPubMed

133.

Wu P, Mo Y, Peng M, Tang T, Zhong Y, Deng X, et al. Emerging role of tumor-related functional peptides encoded by lncRNA and circRNA. Mol Cancer. 2020;19:22. https://doi.org/10.1186/s12943-020-1147-3.CrossRefPubMedPubMedCentral

134.

Fan CM, Wang JP, Tang YY, Zhao J, He SY, Xiong F, et al. circMAN1A2 could serve as a novel serum biomarker for malignant tumors. Cancer Sci. 2019;110:2180–88. https://doi.org/10.1111/cas.14034.CrossRefPubMedPubMedCentral

135.

Fan C, Qu H, Xiong F, Tang Y, Tang T, Zhang L, et al. CircARHGAP12 promotes nasopharyngeal carcinoma migration and invasion via ezrin-mediated cytoskeletal remodeling. Cancer Lett. 2021;496:41–56. https://doi.org/10.1016/j.canlet.2020.09.006.

136.

Wang D, Zeng Z, Zhang S, Xiong F, He B, Wu Y, et al. Epstein-Barr virus-encoded miR-BART6-3p inhibits cancer cell proliferation through the LOC553103-STMN1 axis. FASEB J. 2020;34:8012–27. https://doi.org/10.1096/fj.202000039RR.

137.

Wu Y, Wang D, Wei F, Xiong F, Zhang S, Gong Z, et al. EBV-miR-BART12 accelerates migration and invasion in EBV-associated cancer cells by targeting tubulin polymerization-promoting protein 1. FASEB J. 2020;34:16205–23. https://doi.org/10.1096/fj.202001508R.

138.

Fan X, Zhang X, Wu X, Guo H, Hu Y, Tang F, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 2015;16:148. https://doi.org/10.1186/s13059-015-0706-1.

Title: Single‐cell RNA sequencing in cancer research
Authors: Yijie Zhang
Dan Wang
Miao Peng
Le Tang
Jiawei Ouyang
Fang Xiong
Can Guo
Yanyan Tang
Yujuan Zhou
Qianjin Liao
Xu Wu
Hui Wang
Jianjun Yu
Yong Li
Xiaoling Li
Guiyuan Li
Zhaoyang Zeng
Yixin Tan
Wei Xiong
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Metastasis
Published in: Journal of Experimental & Clinical Cancer Research / Issue 1/2021
Electronic ISSN: 1756-9966
DOI: https://doi.org/10.1186/s13046-021-01874-1

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 1/2021

Potential therapeutic targets in the tumor microenvironment of hepatocellular carcinoma: reversing the protumor effect of tumor-associated macrophages

Downregulation of lncRNA ZNF582-AS1 due to DNA hypermethylation promotes clear cell renal cell carcinoma growth and metastasis by regulating the N(6)-methyladenosine modification of MT-RNR1

HIF-1α switches the functionality of TGF-β signaling via changing the partners of smads to drive glucose metabolic reprogramming in non-small cell lung cancer

USP16 regulates castration-resistant prostate cancer cell proliferation by deubiquitinating and stablizing c-Myc

Cell surface Nucleolin represents a novel cellular target for neuroblastoma therapy

ATAD2 interacts with C/EBPβ to promote esophageal squamous cell carcinoma metastasis via TGF-β1/Smad3 signaling

Keynote webinar | Spotlight on antibody–drug conjugates in cancer