The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy

Abstract

The international American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) tumour-node-metastasis (TNM) staging system provides the current guidelines for the classification of cancer. However, among patients within the same stage, the clinical outcome can be very different. More recently, a novel definition of cancer has emerged, implicating at all stages a complex and dynamic interaction between tumour cells and the immune system. This has enabled the definition of the immune contexture, representing the pre-existing immune parameters associated with patient survival. Even so, the role of distinct immune cell types in modulating cancer progression is increasingly emerging. An immune-based assay named the ‘Immunoscore’ was defined to quantify the in situ T cell infiltrate and was demonstrated to be superior to the AJCC/UICC TNM classification for patients with colorectal cancer. This Review provides a broad overview of the main immune parameters positively or negatively shaping cancer development, including the Immunoscore, and their prognostic and predictive value. The importance of the immune system in cancer control is demonstrated by the requirement for a pre-existing intratumour adaptive immune response for effective immunotherapies, such as checkpoint inhibitors. Finally, we discuss how the combination of multiple immune parameters, rather than individual ones, might increase prognostic and/or predictive power.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genomic, biological and aetiological features associated with human cancer.
Fig. 2: Effects of the immune infiltrate on the prognosis of patients with cancer.
Fig. 3: The immune contexture.
Fig. 4: Overlap between immune signatures.
Fig. 5: Mechanistic immune signatures.

References

  1. 1.

    Locker, G. Y. et al. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J. Clin. Oncol. 24, 5313–5327 (2006).

    CAS  PubMed  Google Scholar 

  2. 2.

    Sobin, L. & Wittekind, C. TNM Classification of Malignant Tumors 6th edn (Wiley-Liss, 2002).

  3. 3.

    Weitz, J. et al. Colorectal cancer. Lancet 365, 153–165 (2005).

    PubMed  Google Scholar 

  4. 4.

    Cadiz, F., Gormaz, J. G. & Burotto, M. Breast cancer staging: is TNM ready to evolve? J. Glob. Oncol. 4, 1–3 (2018).

    PubMed  Google Scholar 

  5. 5.

    Hattori, A., Takamochi, K., Oh, S. & Suzuki, K. New revisions and current issues in the eighth edition of the TNM classification for non-small cell lung cancer. Jpn. J. Clin. Oncol. 49, 3–11 (2019).

    PubMed  Google Scholar 

  6. 6.

    Loibl, S. & Gianni, L. HER2-positive breast cancer. Lancet 389, 2415–2429 (2017).

    CAS  PubMed  Google Scholar 

  7. 7.

    Nagtegaal, I. D., Quirke, P. & Schmoll, H. J. Has the new TNM classification for colorectal cancer improved care? Nat. Rev. Clin. Oncol. 9, 119–123 (2011).

    PubMed  Google Scholar 

  8. 8.

    Khatamianfar, V. et al. TRIM59, a novel multiple cancer biomarker for immunohistochemical detection of tumorigenesis. BMJ Open 2, e001410 (2012).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Seo, A. N. et al. Novel EGFR mutation-specific antibodies for lung adenocarcinoma: highly specific but not sensitive detection of an E746_A750 deletion in exon 19 and an L858R mutation in exon 21 by immunohistochemistry. Lung Cancer 83, 316–323 (2014).

    PubMed  Google Scholar 

  10. 10.

    Ilie, M. et al. Diagnostic value of immunohistochemistry for the detection of the BRAFV600E mutation in primary lung adenocarcinoma Caucasian patients. Ann. Oncol. 24, 742–748 (2013).

    CAS  PubMed  Google Scholar 

  11. 11.

    Ilie, M., Hofman, V., Dietel, M., Soria, J. C. & Hofman, P. Assessment of the PD-L1 status by immunohistochemistry: challenges and perspectives for therapeutic strategies in lung cancer patients. Virchows Arch. 468, 511–525 (2016).

    CAS  PubMed  Google Scholar 

  12. 12.

    Ferreira-Facio, C. S. et al. Contribution of multiparameter flow cytometry immunophenotyping to the diagnostic screening and classification of pediatric cancer. PLoS ONE 8, e55534 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Shen, H. et al. Staging and monitoring of childhood rhabdomyosarcoma with flow cytometry. Oncol. Lett. 7, 970–976 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Hayry, V. et al. Rapid nodal staging of head and neck cancer surgical specimens with flow cytometric analysis. Br. J. Cancer 118, 421–427 (2018).

    PubMed  Google Scholar 

  15. 15.

    Garattini, S. K. et al. Molecular classifications of gastric cancers: novel insights and possible future applications. World J. Gastrointest. Oncol. 9, 194–208 (2017).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Mamatjan, Y. et al. Molecular signatures for tumor classification: an analysis of the cancer genome atlas data. J. Mol. Diagn. 19, 881–891 (2017).

    CAS  PubMed  Google Scholar 

  17. 17.

    Yanovich, G. et al. Clinical proteomics of breast cancer reveals a novel layer of breast cancer classification. Cancer Res. 78, 6001–6010 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    McAlpine, J., Leon-Castillo, A. & Bosse, T. The rise of a novel classification system for endometrial carcinoma; integration of molecular subclasses. J. Pathol. 244, 538–549 (2018).

    PubMed  Google Scholar 

  19. 19.

    Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006). This article demonstrates the dependence of tumour progression and invasion on intratumoural adaptive immunity. It shows that T cell infiltrates and IFNγ signatures have predictive value superior to TNM staging with respect to the natural history of primary cancers.

    CAS  PubMed  Google Scholar 

  20. 20.

    Galon, J., Angell, H. K., Bedognetti, D. & Marincola, F. M. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 39, 11–26 (2013).

    CAS  PubMed  Google Scholar 

  21. 21.

    Galon, J., Fridman, W. H. & Pages, F. The adaptive immunologic microenvironment in colorectal cancer: a novel perspective. Cancer Res. 67, 1883–1886 (2007).

    CAS  PubMed  Google Scholar 

  22. 22.

    Palmieri, G. et al. Genetic instability and increased mutational load: which diagnostic tool best direct patients with cancer to immunotherapy? J. Transl. Med. 15, 17 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Biller, L. H., Syngal, S. & Yurgelun, M. B. Recent advances in Lynch syndrome. Fam. Cancer 18, 211–219 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Yuza, K., Nagahashi, M., Watanabe, S., Takabe, K. & Wakai, T. Hypermutation and microsatellite instability in gastrointestinal cancers. Oncotarget 8, 112103–112115 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Ben-David, U. & Amon, A. Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62 (2020).

    CAS  PubMed  Google Scholar 

  26. 26.

    Sonugur, F. G. & Akbulut, H. The role of tumor microenvironment in genomic instability of malignant tumors. Front. Genet. 10, 1063 (2019).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Galon, J. et al. Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J. Pathol. 232, 199–209 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Ogino, S. et al. CpG island methylator phenotype (CIMP) of colorectal cancer is best characterised by quantitative DNA methylation analysis and prospective cohort studies. Gut 55, 1000–1006 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Zlobec, I., Bihl, M., Foerster, A., Rufle, A. & Lugli, A. Comprehensive analysis of CpG island methylator phenotype (CIMP)-high, -low, and -negative colorectal cancers based on protein marker expression and molecular features. J. Pathol. 225, 336–343 (2011).

    CAS  PubMed  Google Scholar 

  30. 30.

    Cantalupo, P. G., Katz, J. P. & Pipas, J. M. Viral sequences in human cancer. Virology 513, 208–216 (2018).

    CAS  PubMed  Google Scholar 

  31. 31.

    Mesri, E. A., Feitelson, M. A. & Munger, K. Human viral oncogenesis: a cancer hallmarks analysis. Cell Host Microbe 15, 266–282 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Brindley, P. J., da Costa, J. M. & Sripa, B. Why does infection with some helminths cause cancer? Trends Cancer 1, 174–182 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ishaq, S. & Nunn, L. Helicobacter pylori and gastric cancer: a state of the art review. Gastroenterol. Hepatol. Bed Bench 8, S6–S14 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Mui, U. N., Haley, C. T. & Tyring, S. K. Viral oncology: molecular biology and pathogenesis. J. Clin. Med. 6, 111 (2017).

    PubMed Central  Google Scholar 

  35. 35.

    Global Burden of Disease Cancer Collaboration. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol. 3, 524–548 (2017).

    PubMed Central  Google Scholar 

  36. 36.

    Hecht, S. S. Tobacco carcinogens, their biomarkers and tobacco-induced cancer. Nat. Rev. Cancer 3, 733–744 (2003).

    CAS  PubMed  Google Scholar 

  37. 37.

    Narayanan, D. L., Saladi, R. N. & Fox, J. L. Ultraviolet radiation and skin cancer. Int. J. Dermatol. 49, 978–986 (2010).

    PubMed  Google Scholar 

  38. 38.

    Ishizuka, M., Nagata, H., Takagi, K., Iwasaki, Y. & Kubota, K. Combination of platelet count and neutrophil to lymphocyte ratio is a useful predictor of postoperative survival in patients with colorectal cancer. Br. J. Cancer 109, 401–407 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. Cancer-related inflammation. Nature 454, 436–444 (2008).

    CAS  Google Scholar 

  40. 40.

    Stotz, M. et al. Increased neutrophil-lymphocyte ratio is a poor prognostic factor in patients with primary operable and inoperable pancreatic cancer. Br. J. Cancer 109, 416–421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Gong, J., Chehrazi-Raffle, A., Reddi, S. & Salgia, R. Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J. Immunother. Cancer 6, 8 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Zhou, W., Huang, C. & Yuan, N. Prognostic nomograms based on log odds of positive lymph nodes for patients with renal cell carcinoma: a retrospective cohort study. Int. J. Surg. 60, 28–40 (2018).

    CAS  PubMed  Google Scholar 

  43. 43.

    Angell, H. & Galon, J. From the immune contexture to the Immunoscore: the role of prognostic and predictive immune markers in cancer. Curr. Opin. Immunol. 25, 261–267 (2013).

    CAS  PubMed  Google Scholar 

  44. 44.

    Janssen, L. M. E., Ramsay, E. E., Logsdon, C. D. & Overwijk, W. W. The immune system in cancer metastasis: friend or foe? J. Immunother. Cancer 5, 79 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Upadhyay, S., Sharma, N., Gupta, K. B. & Dhiman, M. Role of immune system in tumor progression and carcinogenesis. J. Cell Biochem. 119, 5028–5042 (2018).

    CAS  PubMed  Google Scholar 

  46. 46.

    Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    CAS  PubMed  Google Scholar 

  47. 47.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  Google Scholar 

  48. 48.

    Bindea, G., Mlecnik, B., Fridman, W. H., Pages, F. & Galon, J. Natural immunity to cancer in humans. Curr. Opin. Immunol. 22, 215–222 (2010).

    CAS  PubMed  Google Scholar 

  49. 49.

    Strimbu, K. & Tavel, J. A. What are biomarkers? Curr. Opin. HIV AIDS 5, 463–466 (2010).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Oldenhuis, C. N., Oosting, S. F., Gietema, J. A. & de Vries, E. G. Prognostic versus predictive value of biomarkers in oncology. Eur. J. Cancer 44, 946–953 (2008).

    CAS  PubMed  Google Scholar 

  51. 51.

    Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).

    CAS  PubMed  Google Scholar 

  52. 52.

    Fridman, W. H., Zitvogel, L., Sautes-Fridman, C. & Kroemer, G. The immune contexture in cancer prognosis and treatment. Nat. Rev. Clin. Oncol. 14, 717–734 (2017).

    CAS  PubMed  Google Scholar 

  53. 53.

    Pages, F. et al. Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene 29, 1093–1102 (2010).

    CAS  PubMed  Google Scholar 

  54. 54.

    Hu, Z., Gu, X., Zhong, R. & Zhong, H. Tumor-infiltrating CD45RO+ memory cells correlate with favorable prognosis in patients with lung adenocarcinoma. J. Thorac. Dis. 10, 2089–2099 (2018).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Giraldo, N. A. et al. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin. Cancer Res. 21, 3031–3040 (2015).

    CAS  PubMed  Google Scholar 

  56. 56.

    Remark, R. et al. Characteristics and clinical impacts of the immune environments in colorectal and renal cell carcinoma lung metastases: influence of tumor origin. Clin. Cancer Res. 19, 4079–4091 (2013).

    CAS  PubMed  Google Scholar 

  57. 57.

    Petitprez, F. et al. PD-L1 expression and CD8+ T-cell infiltrate are associated with clinical progression in patients with node-positive prostate cancer. Eur. Urol. Focus 5, 192–196 (2019).

    PubMed  Google Scholar 

  58. 58.

    Granier, C. et al. Tim-3 expression on tumor-infiltrating PD-1+CD8+ T cells correlates with poor clinical outcome in renal cell carcinoma. Cancer Res. 77, 1075–1082 (2017).

    CAS  PubMed  Google Scholar 

  59. 59.

    Nakano, O. et al. Proliferative activity of intratumoral CD8+ T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity. Cancer Res. 61, 5132–5136 (2001).

    CAS  PubMed  Google Scholar 

  60. 60.

    Huang, J., Shen, F., Huang, H., Ling, C. & Zhang, G. Th1high in tumor microenvironment is an indicator of poor prognosis for patients with NSCLC. Oncotarget 8, 13116–13125 (2017).

    PubMed  Google Scholar 

  61. 61.

    Stoll, G. et al. Meta-analysis of organ-specific differences in the structure of the immune infiltrate in major malignancies. Oncotarget 6, 11894–11909 (2015).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    De Monte, L. et al. Intratumor T helper type 2 cell infiltrate correlates with cancer-associated fibroblast thymic stromal lymphopoietin production and reduced survival in pancreatic cancer. J. Exp. Med. 208, 469–478 (2011).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Takashima, Y., Kawaguchi, A., Kanayama, T., Hayano, A. & Yamanaka, R. Correlation between lower balance of Th2 helper T-cells and expression of PD-L1/PD-1 axis genes enables prognostic prediction in patients with glioblastoma. Oncotarget 9, 19065–19078 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Schreck, S. et al. Prognostic impact of tumour-infiltrating Th2 and regulatory T cells in classical Hodgkin lymphoma. Hematol. Oncol. 27, 31–39 (2009).

    CAS  PubMed  Google Scholar 

  65. 65.

    Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Tosolini, M. 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. 71, 1263–1271 (2011).

    CAS  PubMed  Google Scholar 

  67. 67.

    Chen, X. et al. Increased IL-17-producing cells correlate with poor survival and lymphangiogenesis in NSCLC patients. Lung Cancer 69, 348–354 (2010).

    PubMed  Google Scholar 

  68. 68.

    Zhang, J. P. et al. Increased intratumoral IL-17-producing cells correlate with poor survival in hepatocellular carcinoma patients. J. Hepatol. 50, 980–989 (2009).

    CAS  PubMed  Google Scholar 

  69. 69.

    Lv, L. et al. The accumulation and prognosis value of tumor infiltrating IL-17 producing cells in esophageal squamous cell carcinoma. PLoS ONE 6, e18219 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Wang, J. T. et al. Intratumoral IL17-producing cells infiltration correlate with antitumor immune contexture and improved response to adjuvant chemotherapy in gastric cancer. Ann. Oncol. 30, 266–273 (2019).

    CAS  PubMed  Google Scholar 

  71. 71.

    Kryczek, I. et al. Phenotype, distribution, generation, and functional and clinical relevance of Th17 cells in the human tumor environments. Blood 114, 1141–1149 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Punt, S. et al. Angels and demons: Th17 cells represent a beneficial response, while neutrophil IL-17 is associated with poor prognosis in squamous cervical cancer. Oncoimmunology 4, e984539 (2015).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Fabre, J. et al. Targeting the tumor microenvironment: the protumor effects of IL-17 related to cancer type. Int. J. Mol. Sci. 17, 1433 (2016).

    PubMed Central  Google Scholar 

  74. 74.

    Liu, X. et al. Intratumor IL-17-positive mast cells are the major source of the IL-17 that is predictive of survival in gastric cancer patients. PLoS ONE 9, e106834 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Beringer, A., Noack, M. & Miossec, P. IL-17 in chronic inflammation: from discovery to targeting. Trends Mol. Med. 22, 230–241 (2016).

    CAS  PubMed  Google Scholar 

  76. 76.

    Chehimi, M., Vidal, H. & Eljaafari, A. Pathogenic role of IL-17-producing immune cells in obesity, and related inflammatory diseases. J. Clin. Med. 6, 68 (2017).

    PubMed Central  Google Scholar 

  77. 77.

    Amicarella, F. et al. Dual role of tumour-infiltrating T helper 17 cells in human colorectal cancer. Gut 66, 692–704 (2017).

    CAS  PubMed  Google Scholar 

  78. 78.

    Schmetterer, K. G., Neunkirchner, A. & Pickl, W. F. Naturally occurring regulatory T cells: markers, mechanisms, and manipulation. FASEB J. 26, 2253–2276 (2012).

    CAS  PubMed  Google Scholar 

  79. 79.

    Li, J. F. et al. The prognostic value of peritumoral regulatory T cells and its correlation with intratumoral cyclooxygenase-2 expression in clear cell renal cell carcinoma. BJU Int. 103, 399–405 (2009).

    PubMed  Google Scholar 

  80. 80.

    Shah, W. et al. A reversed CD4/CD8 ratio of tumor-infiltrating lymphocytes and a high percentage of CD4+FOXP3+ regulatory T cells are significantly associated with clinical outcome in squamous cell carcinoma of the cervix. Cell Mol. Immunol. 8, 59–66 (2011).

    PubMed  Google Scholar 

  81. 81.

    Siddiqui, S. A. et al. Tumor-infiltrating Foxp3-CD4+CD25+ T cells predict poor survival in renal cell carcinoma. Clin. Cancer Res. 13, 2075–2081 (2007).

    CAS  PubMed  Google Scholar 

  82. 82.

    Grabenbauer, G. G., Lahmer, G., Distel, L. & Niedobitek, G. Tumor-infiltrating cytotoxic T cells but not regulatory T cells predict outcome in anal squamous cell carcinoma. Clin. Cancer Res. 12, 3355–3360 (2006).

    CAS  PubMed  Google Scholar 

  83. 83.

    Heimberger, A. B. et al. Incidence and prognostic impact of FoxP3+ regulatory T cells in human gliomas. Clin. Cancer Res. 14, 5166–5172 (2008).

    CAS  PubMed  Google Scholar 

  84. 84.

    Jacobs, J. F. et al. Prognostic significance and mechanism of Treg infiltration in human brain tumors. J. Neuroimmunol. 225, 195–199 (2010).

    CAS  PubMed  Google Scholar 

  85. 85.

    Badoual, C. et al. Prognostic value of tumor-infiltrating CD4+ T-cell subpopulations in head and neck cancers. Clin. Cancer Res. 12, 465–472 (2006).

    CAS  PubMed  Google Scholar 

  86. 86.

    Carreras, J. et al. High numbers of tumor-infiltrating FOXP3-positive regulatory T cells are associated with improved overall survival in follicular lymphoma. Blood 108, 2957–2964 (2006).

    CAS  PubMed  Google Scholar 

  87. 87.

    Frey, D. M. et al. High frequency of tumor-infiltrating FOXP3+ regulatory T cells predicts improved survival in mismatch repair-proficient colorectal cancer patients. Int. J. Cancer 126, 2635–2643 (2010).

    CAS  PubMed  Google Scholar 

  88. 88.

    Leffers, N. et al. Prognostic significance of tumor-infiltrating T-lymphocytes in primary and metastatic lesions of advanced stage ovarian cancer. Cancer Immunol. Immunother. 58, 449–459 (2009).

    PubMed  Google Scholar 

  89. 89.

    Salama, P. et al. Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer. J. Clin. Oncol. 27, 186–192 (2009).

    PubMed  Google Scholar 

  90. 90.

    Tzankov, A. et al. Correlation of high numbers of intratumoral FOXP3+ regulatory T cells with improved survival in germinal center-like diffuse large B-cell lymphoma, follicular lymphoma and classical Hodgkin’s lymphoma. Haematologica 93, 193–200 (2008).

    CAS  PubMed  Google Scholar 

  91. 91.

    Winerdal, M. E. et al. FOXP3 and survival in urinary bladder cancer. BJU Int. 108, 1672–1678 (2011).

    CAS  PubMed  Google Scholar 

  92. 92.

    Curiel, T. J. et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat. Med. 10, 942–949 (2004).

    CAS  PubMed  Google Scholar 

  93. 93.

    Shang, B., Liu, Y., Jiang, S. J. & Liu, Y. Prognostic value of tumor-infiltrating FoxP3+ regulatory T cells in cancers: a systematic review and meta-analysis. Sci. Rep. 5, 15179 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sun, L. et al. Clinicopathologic and prognostic significance of regulatory T cells in patients with hepatocellular carcinoma: a meta-analysis. Oncotarget 8, 39658–39672 (2017).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Hillen, F. et al. Leukocyte infiltration and tumor cell plasticity are parameters of aggressiveness in primary cutaneous melanoma. Cancer Immunol. Immunother. 57, 97–106 (2008).

    PubMed  Google Scholar 

  96. 96.

    Ladanyi, A. et al. FOXP3+ cell density in primary tumor has no prognostic impact in patients with cutaneous malignant melanoma. Pathol. Oncol. Res. 16, 303–309 (2010).

    PubMed  Google Scholar 

  97. 97.

    Mahmoud, S. M. et al. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J. Clin. Oncol. 29, 1949–1955 (2011).

    PubMed  Google Scholar 

  98. 98.

    Mizukami, Y. et al. Localisation pattern of Foxp3+ regulatory T cells is associated with clinical behaviour in gastric cancer. Br. J. Cancer 98, 148–153 (2008).

    CAS  PubMed  Google Scholar 

  99. 99.

    Shou, J., Zhang, Z., Lai, Y., Chen, Z. & Huang, J. Worse outcome in breast cancer with higher tumor-infiltrating FOXP3+ Tregs: a systematic review and meta-analysis. BMC Cancer 16, 687 (2016).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Shitara, K. & Nishikawa, H. Regulatory T cells: a potential target in cancer immunotherapy. Ann. NY Acad. Sci. 1417, 104–115 (2018).

    CAS  PubMed  Google Scholar 

  101. 101.

    Saito, T. et al. Two FOXP3+CD4+ T cell subpopulations distinctly control the prognosis of colorectal cancers. Nat. Med. 22, 679–684 (2016).

    CAS  PubMed  Google Scholar 

  102. 102.

    Posselt, R. et al. Spatial distribution of FoxP3+ and CD8+ tumour infiltrating T cells reflects their functional activity. Oncotarget 7, 60383–60394 (2016).

    PubMed  PubMed Central  Google Scholar 

  103. 103.

    Vinuesa, C. G., Linterman, M. A., Yu, D. & MacLennan, I. C. Follicular helper T cells. Annu. Rev. Immunol. 34, 335–368 (2016).

    CAS  PubMed  Google Scholar 

  104. 104.

    Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013). This article describes the immune signatures of purified immune cell subpopulations in human tumours. It shows that immune infiltrate composition changes at each tumour stage and T cells, B cells and T FH cells impact survival.

    CAS  PubMed  Google Scholar 

  105. 105.

    Gu-Trantien, C. et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 123, 2873–2892 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Gu-Trantien, C. & Willard-Gallo, K. PD-1hiCXCR5-CD4+ TFH cells play defense in cancer and offense in arthritis. Trends Immunol. 38, 875–878 (2017).

    CAS  PubMed  Google Scholar 

  107. 107.

    Jia, Y. et al. Impaired function of CD4+ T follicular helper (Tfh) cells associated with hepatocellular carcinoma progression. PLoS ONE 10, e0117458 (2015).

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Smyth, M. J. et al. NKG2D function protects the host from tumor initiation. J. Exp. Med. 202, 583–588 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Mandal, A. & Viswanathan, C. Natural killer cells: In health and disease. Hematol. Oncol. Stem Cell Ther. 8, 47–55 (2015).

    CAS  PubMed  Google Scholar 

  110. 110.

    Sun, J. C. & Lanier, L. L. NK cell development, homeostasis and function: parallels with CD8+ T cells. Nat. Rev. Immunol. 11, 645–657 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Vivier, E. et al. Innate or adaptive immunity? The example of natural killer cells. Science 331, 44–49 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Morandi, B. et al. Dendritic cell editing by activated natural killer cells results in a more protective cancer-specific immune response. PLoS ONE 7, e39170 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Bottcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037 e14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Fessenden, T. B., Duong, E. & Spranger, S. A team effort: natural killer cells on the first leg of the tumor immunity relay race. J. Immunother. Cancer 6, 67 (2018).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Zhu, L. Y., Zhou, J., Liu, Y. Z. & Pan, W. D. [Prognostic significance of natural killer cell infiltration in hepatocellular carcinoma]. Ai Zheng 28, 1198–1202 (2009).

    PubMed  Google Scholar 

  116. 116.

    Coca, S. et al. The prognostic significance of intratumoral natural killer cells in patients with colorectal carcinoma. Cancer 79, 2320–2328 (1997).

    CAS  PubMed  Google Scholar 

  117. 117.

    Menon, A. G. et al. Immune system and prognosis in colorectal cancer: a detailed immunohistochemical analysis. Lab. Invest. 84, 493–501 (2004).

    CAS  PubMed  Google Scholar 

  118. 118.

    Melero, I., Rouzaut, A., Motz, G. T. & Coukos, G. T-cell and NK-cell infiltration into solid tumors: a key limiting factor for efficacious cancer immunotherapy. Cancer Discov. 4, 522–526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Eckl, J. et al. Transcript signature predicts tissue NK cell content and defines renal cell carcinoma subgroups independent of TNM staging. J. Mol. Med. 90, 55–66 (2012).

    CAS  PubMed  Google Scholar 

  120. 120.

    Pasero, C. et al. Highly effective NK cells are associated with good prognosis in patients with metastatic prostate cancer. Oncotarget 6, 14360–14373 (2015).

    PubMed  PubMed Central  Google Scholar 

  121. 121.

    Mundy-Bosse, B. et al. Highly cytotoxic natural killer cells are associated with poor prognosis in patients with cutaneous T-cell lymphoma. Blood Adv. 2, 1818–1827 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Sun, H. et al. Human CD96 correlates to NK cell exhaustion and predicts the prognosis of human hepatocellular carcinoma. Hepatology 70, 168–183 (2019).

    CAS  PubMed  Google Scholar 

  123. 123.

    Nair, S. & Dhodapkar, M. V. Natural killer T cells in cancer immunotherapy. Front. Immunol. 8, 1178 (2017).

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Mussai, F., De Santo, C. & Cerundolo, V. Interaction between invariant NKT cells and myeloid-derived suppressor cells in cancer patients: evidence and therapeutic opportunities. J. Immunother. 35, 449–459 (2012).

    CAS  PubMed  Google Scholar 

  125. 125.

    Song, L. et al. Valpha24-invariant NKT cells mediate antitumor activity via killing of tumor-associated macrophages. J. Clin. Invest. 119, 1524–1536 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Bae, E. A. et al. Activation of NKT cells in an anti-PD-1-resistant tumor model enhances antitumor immunity by reinvigorating exhausted CD8 T cells. Cancer Res. 78, 5315–5326 (2018).

    CAS  PubMed  Google Scholar 

  127. 127.

    Metelitsa, L. S. et al. Natural killer T cells infiltrate neuroblastomas expressing the chemokine CCL2. J. Exp. Med. 199, 1213–1221 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Tachibana, T. et al. Increased intratumor Valpha24-positive natural killer T cells: a prognostic factor for primary colorectal carcinomas. Clin. Cancer Res. 11, 7322–7327 (2005).

    CAS  PubMed  Google Scholar 

  129. 129.

    Schneiders, F. L. et al. Circulating invariant natural killer T-cell numbers predict outcome in head and neck squamous cell carcinoma: updated analysis with 10-year follow-up. J. Clin. Oncol. 30, 567–570 (2012).

    PubMed  Google Scholar 

  130. 130.

    Gorini, F. et al. Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis. Blood 129, 3440–3451 (2017).

    CAS  PubMed  Google Scholar 

  131. 131.

    Wculek, S. K. et al. Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 20, 7–24 (2020).

    CAS  PubMed  Google Scholar 

  132. 132.

    Sandel, M. H. et al. Prognostic value of tumor-infiltrating dendritic cells in colorectal cancer: role of maturation status and intratumoral localization. Clin. Cancer Res. 11, 2576–2582 (2005).

    CAS  PubMed  Google Scholar 

  133. 133.

    Malietzis, G. et al. Prognostic value of the tumour-infiltrating dendritic cells in colorectal cancer: a systematic review. Cell Commun. Adhes. 22, 9–14 (2015).

    CAS  PubMed  Google Scholar 

  134. 134.

    Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135.

    Movassagh, M. et al. Selective accumulation of mature DC-Lamp+ dendritic cells in tumor sites is associated with efficient T-cell-mediated antitumor response and control of metastatic dissemination in melanoma. Cancer Res. 64, 2192–2198 (2004).

    CAS  PubMed  Google Scholar 

  136. 136.

    Truxova, I. et al. Mature dendritic cells correlate with favorable immune infiltrate and improved prognosis in ovarian carcinoma patients. J. Immunother. Cancer 6, 139 (2018).

    PubMed  PubMed Central  Google Scholar 

  137. 137.

    Tran Janco, J. M., Lamichhane, P., Karyampudi, L. & Knutson, K. L. Tumor-infiltrating dendritic cells in cancer pathogenesis. J. Immunol. 194, 2985–2991 (2015).

    PubMed  Google Scholar 

  138. 138.

    Sisirak, V. et al. Impaired IFN-alpha production by plasmacytoid dendritic cells favors regulatory T-cell expansion that may contribute to breast cancer progression. Cancer Res. 72, 5188–5197 (2012).

    CAS  PubMed  Google Scholar 

  139. 139.

    Demoulin, S., Herfs, M., Delvenne, P. & Hubert, P. Tumor microenvironment converts plasmacytoid dendritic cells into immunosuppressive/tolerogenic cells: insight into the molecular mechanisms. J. Leukoc. Biol. 93, 343–352 (2013).

    CAS  PubMed  Google Scholar 

  140. 140.

    Lombardi, V. C., Khaiboullina, S. F. & Rizvanov, A. A. Plasmacytoid dendritic cells, a role in neoplastic prevention and progression. Eur. J. Clin. Invest. 45 (Suppl. 1), 1–8 (2015).

    CAS  PubMed  Google Scholar 

  141. 141.

    Le Mercier, I. et al. Tumor promotion by intratumoral plasmacytoid dendritic cells is reversed by TLR7 ligand treatment. Cancer Res. 73, 4629–4640 (2013).

    PubMed  Google Scholar 

  142. 142.

    Kini Bailur, J., Gueckel, B. & Pawelec, G. Prognostic impact of high levels of circulating plasmacytoid dendritic cells in breast cancer. J. Transl. Med. 14, 151 (2016).

    PubMed  PubMed Central  Google Scholar 

  143. 143.

    Liu, W. et al. Gastric cancer patients have elevated plasmacytoid and CD1c+ dendritic cells in the peripheral blood. Oncol. Lett. 15, 5087–5092 (2018).

    PubMed  PubMed Central  Google Scholar 

  144. 144.

    Aras, S. & Zaidi, M. R. TAMeless traitors: macrophages in cancer progression and metastasis. Br. J. Cancer 117, 1583–1591 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Martinez, F. O., Helming, L. & Gordon, S. Alternative activation of macrophages: an immunologic functional perspective. Annu. Rev. Immunol. 27, 451–483 (2009).

    CAS  PubMed  Google Scholar 

  146. 146.

    Mosser, D. M. & Edwards, J. P. Exploring the full spectrum of macrophage activation. Nat. Rev. Immunol. 8, 958–969 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Forssell, J. et al. High macrophage infiltration along the tumor front correlates with improved survival in colon cancer. Clin. Cancer Res. 13, 1472–1479 (2007).

    CAS  PubMed  Google Scholar 

  148. 148.

    Lackner, C. et al. Prognostic relevance of tumour-associated macrophages and von Willebrand factor-positive microvessels in colorectal cancer. Virchows Arch. 445, 160–167 (2004).

    CAS  PubMed  Google Scholar 

  149. 149.

    Zhou, Q. et al. The density of macrophages in the invasive front is inversely correlated to liver metastasis in colon cancer. J. Transl. Med. 8, 13 (2010).

    PubMed  PubMed Central  Google Scholar 

  150. 150.

    Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    DeNardo, D. G. & Ruffell, B. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 19, 369–382 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Cassetta, L. & Pollard, J. W. Targeting macrophages: therapeutic approaches in cancer. Nat. Rev. Drug Discov. 17, 887–904 (2018).

    CAS  PubMed  Google Scholar 

  153. 153.

    Zhao, X. et al. Prognostic significance of tumor-associated macrophages in breast cancer: a meta-analysis of the literature. Oncotarget 8, 30576–30586 (2017).

    PubMed  PubMed Central  Google Scholar 

  154. 154.

    Yin, S. et al. The prognostic and clinicopathological significance of tumor-associated macrophages in patients with gastric cancer: a meta-analysis. PLoS ONE 12, e0170042 (2017).

    PubMed  PubMed Central  Google Scholar 

  155. 155.

    Zhang, Q. W. et al. Prognostic significance of tumor-associated macrophages in solid tumor: a meta-analysis of the literature. PLoS ONE 7, e50946 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Guo, B., Cen, H., Tan, X. & Ke, Q. Meta-analysis of the prognostic and clinical value of tumor-associated macrophages in adult classical Hodgkin lymphoma. BMC Med. 14, 159 (2016).

    PubMed  PubMed Central  Google Scholar 

  157. 157.

    Steidl, C. et al. Tumor-associated macrophages and survival in classic Hodgkin’s lymphoma. N. Engl. J. Med. 362, 875–885 (2010). The study shows that an increased number of TAMs was strongly associated with shortened survival in patients with classic Hodgkin lymphoma.

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Alvaro, T. et al. The presence of STAT1-positive tumor-associated macrophages and their relation to outcome in patients with follicular lymphoma. Haematologica 91, 1605–1612 (2006).

    CAS  PubMed  Google Scholar 

  159. 159.

    Krausgruber, T. et al. IRF5 promotes inflammatory macrophage polarization and TH1-TH17 responses. Nat. Immunol. 12, 231–238 (2011).

    CAS  PubMed  Google Scholar 

  160. 160.

    Kinouchi, M. et al. Infiltration of CD40-positive tumor-associated macrophages indicates a favorable prognosis in colorectal cancer patients. Hepatogastroenterology 60, 83–88 (2013).

    CAS  PubMed  Google Scholar 

  161. 161.

    Mei, J. et al. Prognostic impact of tumor-associated macrophage infiltration in non-small cell lung cancer: a systemic review and meta-analysis. Oncotarget 7, 34217–34228 (2016).

    PubMed  PubMed Central  Google Scholar 

  162. 162.

    Gabrilovich, D. I., Ostrand-Rosenberg, S. & Bronte, V. Coordinated regulation of myeloid cells by tumours. Nat. Rev. Immunol. 12, 253–268 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Veglia, F., Perego, M. & Gabrilovich, D. Myeloid-derived suppressor cells coming of age. Nat. Immunol. 19, 108–119 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Kumar, V., Patel, S., Tcyganov, E. & Gabrilovich, D. I. The nature of myeloid-derived suppressor cells in the tumor microenvironment. Trends Immunol. 37, 208–220 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. 165.

    Chen, Y., Pan, G., Tian, D., Zhang, Y. & Li, T. Functional analysis of CD14+HLA-DR-/low myeloid-derived suppressor cells in patients with lung squamous cell carcinoma. Oncol. Lett. 14, 349–354 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166.

    Hock, B. D. et al. Renal transplant recipients have elevated frequencies of circulating myeloid-derived suppressor cells. Nephrol. Dial. Transpl. 27, 402–410 (2012).

    CAS  Google Scholar 

  167. 167.

    Mauti, L. A. et al. Myeloid-derived suppressor cells are implicated in regulating permissiveness for tumor metastasis during mouse gestation. J. Clin. Invest. 121, 2794–2807 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    Diaz-Montero, C. M. et al. Increased circulating myeloid-derived suppressor cells correlate with clinical cancer stage, metastatic tumor burden, and doxorubicin-cyclophosphamide chemotherapy. Cancer Immunol. Immunother. 58, 49–59 (2009).

    CAS  PubMed  Google Scholar 

  169. 169.

    Sun, H. L. et al. Increased frequency and clinical significance of myeloid-derived suppressor cells in human colorectal carcinoma. World J. Gastroenterol. 18, 3303–3309 (2012).

    PubMed  PubMed Central  Google Scholar 

  170. 170.

    Zhang, B. et al. Circulating and tumor-infiltrating myeloid-derived suppressor cells in patients with colorectal carcinoma. PLoS ONE 8, e57114 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. 171.

    Huang, A. et al. Increased CD14+HLA-DR-/low myeloid-derived suppressor cells correlate with extrathoracic metastasis and poor response to chemotherapy in non-small cell lung cancer patients. Cancer Immunol. Immunother. 62, 1439–1451 (2013).

    CAS  PubMed  Google Scholar 

  172. 172.

    Arihara, F. et al. Increase in CD14+HLA-DR-/low myeloid-derived suppressor cells in hepatocellular carcinoma patients and its impact on prognosis. Cancer Immunol. Immunother. 62, 1421–1430 (2013).

    CAS  PubMed  Google Scholar 

  173. 173.

    Jordan, K. R. et al. Myeloid-derived suppressor cells are associated with disease progression and decreased overall survival in advanced-stage melanoma patients. Cancer Immunol. Immunother. 62, 1711–1722 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. 174.

    Angell, T. E. et al. Circulating myeloid-derived suppressor cells predict differentiated thyroid cancer diagnosis and extent. Thyroid 26, 381–389 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Yang, G. et al. Accumulation of myeloid-derived suppressor cells (MDSCs) induced by low levels of IL-6 correlates with poor prognosis in bladder cancer. Oncotarget 8, 38378–38388 (2017).

    PubMed  PubMed Central  Google Scholar 

  176. 176.

    Zhang, S. et al. The role of myeloid-derived suppressor cells in patients with solid tumors: a meta-analysis. PLoS ONE 11, e0164514 (2016).

    PubMed  PubMed Central  Google Scholar 

  177. 177.

    Li, X. et al. Neutrophil count is associated with myeloid derived suppressor cell level and presents prognostic value of for hepatocellular carcinoma patients. Oncotarget 8, 24380–24388 (2017).

    PubMed  PubMed Central  Google Scholar 

  178. 178.

    Wang, P. F. et al. Prognostic role of pretreatment circulating MDSCs in patients with solid malignancies: a meta-analysis of 40 studies. Oncoimmunology 7, e1494113 (2018).

    PubMed  PubMed Central  Google Scholar 

  179. 179.

    Marini, O. et al. Identification of granulocytic myeloid-derived suppressor cells (G-MDSCs) in the peripheral blood of Hodgkin and non-Hodgkin lymphoma patients. Oncotarget 7, 27676–27688 (2016).

    PubMed  PubMed Central  Google Scholar 

  180. 180.

    Wang, Z. et al. Tumor-induced CD14+HLA-DR-/low myeloid-derived suppressor cells correlate with tumor progression and outcome of therapy in multiple myeloma patients. Cancer Immunol. Immunother. 64, 389–399 (2015).

    CAS  PubMed  Google Scholar 

  181. 181.

    Wu, C. et al. Prognostic significance of peripheral monocytic myeloid-derived suppressor cells and monocytes in patients newly diagnosed with diffuse large B-cell lymphoma. Int. J. Clin. Exp. Med. 8, 15173–15181 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  182. 182.

    Liu, C. Y. et al. Population alterations of L-arginase- and inducible nitric oxide synthase-expressed CD11b+/CD14-/CD15+/CD33+ myeloid-derived suppressor cells and CD8+ T lymphocytes in patients with advanced-stage non-small cell lung cancer. J. Cancer Res. Clin. Oncol. 136, 35–45 (2010).

    CAS  PubMed  Google Scholar 

  183. 183.

    Marigo, I. et al. T cell cancer therapy requires CD40-CD40L activation of tumor necrosis factor and inducible nitric-oxide-synthase-producing dendritic cells. Cancer Cell 30, 377–390 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. 184.

    Mundy-Bosse, B. L. et al. Distinct myeloid suppressor cell subsets correlate with plasma IL-6 and IL-10 and reduced interferon-alpha signaling in CD4+ T cells from patients with GI malignancy. Cancer Immunol. Immunother. 60, 1269–1279 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. 185.

    Zoso, A. et al. Human fibrocytic myeloid-derived suppressor cells express IDO and promote tolerance via Treg-cell expansion. Eur. J. Immunol. 44, 3307–3319 (2014).

    CAS  PubMed  Google Scholar 

  186. 186.

    Li, F., Zhao, Y., Wei, L., Li, S. & Liu, J. Tumor-infiltrating Treg, MDSC, and IDO expression associated with outcomes of neoadjuvant chemotherapy of breast cancer. Cancer Biol. Ther. 19, 695–705 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  187. 187.

    Munn, D. H. et al. Potential regulatory function of human dendritic cells expressing indoleamine 2,3-dioxygenase. Science 297, 1867–1870 (2002).

    CAS  PubMed  Google Scholar 

  188. 188.

    Anani, W. & Shurin, M. R. Targeting myeloid-derived suppressor cells in cancer. Adv. Exp. Med. Biol. 1036, 105–128 (2017).

    CAS  PubMed  Google Scholar 

  189. 189.

    Fleming, V. et al. Targeting myeloid-derived suppressor cells to bypass tumor-induced immunosuppression. Front. Immunol. 9, 398 (2018).

    PubMed  PubMed Central  Google Scholar 

  190. 190.

    Bronte, V. et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7, 12150 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. 191.

    Condamine, T. et al. Lectin-type oxidized LDL receptor-1 distinguishes population of human polymorphonuclear myeloid-derived suppressor cells in cancer patients. Sci. Immunol. 1, aaf8943 (2016).

    PubMed  PubMed Central  Google Scholar 

  192. 192.

    Yuen, G. J., Demissie, E. & Pillai, S. B lymphocytes and cancer: a love-hate relationship. Trends Cancer 2, 747–757 (2016).

    PubMed  PubMed Central  Google Scholar 

  193. 193.

    Wada, Y., Nakashima, O., Kutami, R., Yamamoto, O. & Kojiro, M. Clinicopathological study on hepatocellular carcinoma with lymphocytic infiltration. Hepatology 27, 407–414 (1998).

    CAS  PubMed  Google Scholar 

  194. 194.

    Ladanyi, A. et al. Prognostic impact of B-cell density in cutaneous melanoma. Cancer Immunol. Immunother. 60, 1729–1738 (2011).

    CAS  PubMed  Google Scholar 

  195. 195.

    Milne, K. et al. Systematic analysis of immune infiltrates in high-grade serous ovarian cancer reveals CD20, FoxP3 and TIA-1 as positive prognostic factors. PLoS ONE 4, e6412 (2009).

    PubMed  PubMed Central  Google Scholar 

  196. 196.

    Nielsen, J. S. et al. CD20+ tumor-infiltrating lymphocytes have an atypical CD27- memory phenotype and together with CD8+ T cells promote favorable prognosis in ovarian cancer. Clin. Cancer Res. 18, 3281–3292 (2012).

    CAS  PubMed  Google Scholar 

  197. 197.

    Al-Shibli, K. I. et al. Prognostic effect of epithelial and stromal lymphocyte infiltration in non-small cell lung cancer. Clin. Cancer Res. 14, 5220–5227 (2008).

    CAS  PubMed  Google Scholar 

  198. 198.

    Nedergaard, B. S., Ladekarl, M., Nyengaard, J. R. & Nielsen, K. A comparative study of the cellular immune response in patients with stage IB cervical squamous cell carcinoma. Low numbers of several immune cell subtypes are strongly associated with relapse of disease within 5 years. Gynecol. Oncol. 108, 106–111 (2008).

    CAS  PubMed  Google Scholar 

  199. 199.

    Petitprez, F. et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 577, 556–560 (2020).

    CAS  PubMed  Google Scholar 

  200. 200.

    Mahmoud, S. M. et al. Tumour-infiltrating macrophages and clinical outcome in breast cancer. J. Clin. Pathol. 65, 159–163 (2012).

    CAS  PubMed  Google Scholar 

  201. 201.

    Xu, Y., Lan, S. & Zheng, Q. Prognostic significance of infiltrating immune cell subtypes in invasive ductal carcinoma of the breast. Tumori 104, 196–201 (2018).

    CAS  PubMed  Google Scholar 

  202. 202.

    Yeong, J. et al. High densities of tumor-associated plasma cells predict improved prognosis in triple negative breast cancer. Front. Immunol. 9, 1209 (2018).

    PubMed  PubMed Central  Google Scholar 

  203. 203.

    Kroeger, D. R., Milne, K. & Nelson, B. H. Tumor-infiltrating plasma cells are associated with tertiary lymphoid structures, cytolytic T-cell responses, and superior prognosis in ovarian cancer. Clin. Cancer Res. 22, 3005–3015 (2016). This article describes the favourable impact of tumour-infiltrating plasma cells on the antitumour immune response.

    CAS  PubMed  Google Scholar 

  204. 204.

    Sakimura, C. et al. B cells in tertiary lymphoid structures are associated with favorable prognosis in gastric cancer. J. Surg. Res. 215, 74–82 (2017).

    CAS  PubMed  Google Scholar 

  205. 205.

    Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    CAS  PubMed  Google Scholar 

  206. 206.

    Lundgren, S., Berntsson, J., Nodin, B., Micke, P. & Jirstrom, K. Prognostic impact of tumour-associated B cells and plasma cells in epithelial ovarian cancer. J. Ovarian Res. 9, 21 (2016).

    PubMed  PubMed Central  Google Scholar 

  207. 207.

    Dong, H. P. et al. NK- and B-cell infiltration correlates with worse outcome in metastatic ovarian carcinoma. Am. J. Clin. Pathol. 125, 451–458 (2006).

    PubMed  Google Scholar 

  208. 208.

    Yang, C. et al. B cells promote tumor progression via STAT3 regulated-angiogenesis. PLoS ONE 8, e64159 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  209. 209.

    Shen, M., Wang, J. & Ren, X. New insights into tumor-infiltrating B lymphocytes in breast cancer: clinical impacts and regulatory mechanisms. Front. Immunol. 9, 470 (2018).

    PubMed  PubMed Central  Google Scholar 

  210. 210.

    Sarvaria, A., Madrigal, J. A. & Saudemont, A. B cell regulation in cancer and anti-tumor immunity. Cell Mol. Immunol. 14, 662–674 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. 211.

    Goc, J., Fridman, W. H., Sautes-Fridman, C. & Dieu-Nosjean, M. C. Characteristics of tertiary lymphoid structures in primary cancers. Oncoimmunology 2, e26836 (2013).

    PubMed  PubMed Central  Google Scholar 

  212. 212.

    Germain, C., Gnjatic, S. & Dieu-Nosjean, M. C. Tertiary lymphoid structure-associated B cells are key players in anti-tumor immunity. Front. Immunol. 6, 67 (2015).

    PubMed  PubMed Central  Google Scholar 

  213. 213.

    Colbeck, E. J., Ager, A., Gallimore, A. & Jones, G. W. Tertiary lymphoid structures in cancer: drivers of antitumor immunity, immunosuppression, or bystander sentinels in disease? Front. Immunol. 8, 1830 (2017).

    PubMed  PubMed Central  Google Scholar 

  214. 214.

    Di Caro, G. et al. Tertiary lymphoid tissue in the tumor microenvironment: from its occurrence to immunotherapeutic implications. Int. Rev. Immunol. 34, 123–133 (2015).

    PubMed  Google Scholar 

  215. 215.

    Goc, J. et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 74, 705–715 (2014).

    CAS  PubMed  Google Scholar 

  216. 216.

    Dieu-Nosjean, M. C. et al. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J. Clin. Oncol. 26, 4410–4417 (2008).

    CAS  PubMed  Google Scholar 

  217. 217.

    Di Caro, G. et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin. Cancer Res. 20, 2147–2158 (2014).

    PubMed  Google Scholar 

  218. 218.

    Liu, X. et al. Distinct tertiary lymphoid structure associations and their prognostic relevance in HER2 positive and negative breast cancers. Oncologist 22, 1316–1324 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. 219.

    Silina, K. et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 78, 1308–1320 (2018).

    CAS  PubMed  Google Scholar 

  220. 220.

    Sautes-Fridman, C. et al. Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front. Immunol. 7, 407 (2016).

    PubMed  PubMed Central  Google Scholar 

  221. 221.

    Finkin, S. et al. Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat. Immunol. 16, 1235–1244 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  222. 222.

    Posch, F. et al. Maturation of tertiary lymphoid structures and recurrence of stage II and III colorectal cancer. Oncoimmunology 7, e1378844 (2018).

    PubMed  Google Scholar 

  223. 223.

    Engelhard, V. H. et al. Immune cell infiltration and tertiary lymphoid structures as determinants of antitumor immunity. J. Immunol. 200, 432–442 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. 224.

    Siebenhaar, F., Redegeld, F. A., Bischoff, S. C., Gibbs, B. F. & Maurer, M. Mast cells as drivers of disease and therapeutic targets. Trends Immunol. 39, 151–162 (2018).

    CAS  PubMed  Google Scholar 

  225. 225.

    Aponte-Lopez, A., Fuentes-Panana, E. M., Cortes-Munoz, D. & Munoz-Cruz, S. Mast cell, the neglected member of the tumor microenvironment: role in breast cancer. J. Immunol. Res. 2018, 2584243 (2018).

    PubMed  PubMed Central  Google Scholar 

  226. 226.

    Dundar, E. et al. The significance and relationship between mast cells and tumour angiogenesis in non-small cell lung carcinoma. J. Int. Med. Res. 36, 88–95 (2008).

    CAS  PubMed  Google Scholar 

  227. 227.

    Ch’ng, S., Wallis, R. A., Yuan, L., Davis, P. F. & Tan, S. T. Mast cells and cutaneous malignancies. Mod. Pathol. 19, 149–159 (2006).

    PubMed  Google Scholar 

  228. 228.

    Tuna, B., Yorukoglu, K., Unlu, M., Mungan, M. U. & Kirkali, Z. Association of mast cells with microvessel density in renal cell carcinomas. Eur. Urol. 50, 530–534 (2006).

    PubMed  Google Scholar 

  229. 229.

    Rajput, A. B. et al. Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: a study of 4,444 cases. Breast Cancer Res. Treat. 107, 249–257 (2008).

    PubMed  Google Scholar 

  230. 230.

    Varricchi, G. et al. Are mast cells MASTers in cancer? Front. Immunol. 8, 424 (2017).

    PubMed  PubMed Central  Google Scholar 

  231. 231.

    Fleischmann, A. et al. Immunological microenvironment in prostate cancer: high mast cell densities are associated with favorable tumor characteristics and good prognosis. Prostate 69, 976–981 (2009).

    CAS  PubMed  Google Scholar 

  232. 232.

    Marech, I. et al. Serum tryptase, mast cells positive to tryptase and microvascular density evaluation in early breast cancer patients: possible translational significance. BMC Cancer 14, 534 (2014).

    PubMed  PubMed Central  Google Scholar 

  233. 233.

    Templeton, A. J. et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J. Natl Cancer Inst. 106, dju124 (2014).

    PubMed  Google Scholar 

  234. 234.

    Powell, D. R. & Huttenlocher, A. Neutrophils in the tumor microenvironment. Trends Immunol. 37, 41–52 (2016).

    CAS  PubMed  Google Scholar 

  235. 235.

    Shen, M. et al. Tumor-associated neutrophils as a new prognostic factor in cancer: a systematic review and meta-analysis. PLoS ONE 9, e98259 (2014).

    PubMed  PubMed Central  Google Scholar 

  236. 236.

    Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  237. 237.

    Mackey, J. B. G., Coffelt, S. B. & Carlin, L. M. Neutrophil maturity in cancer. Front. Immunol. 10, 1912 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  238. 238.

    Galdiero, M. R. et al. Occurrence and significance of tumor-associated neutrophils in patients with colorectal cancer. Int. J. Cancer 139, 446–456 (2016).

    CAS  PubMed  Google Scholar 

  239. 239.

    Lominadze, G. et al. Proteomic analysis of human neutrophil granules. Mol. Cell Proteom. 4, 1503–1521 (2005).

    CAS  Google Scholar 

  240. 240.

    Zhao, L. et al. An enzyme-linked immunosorbent assay for human carcinoembryonic antigen-related cell adhesion molecule 8, a biological marker of granulocyte activities in vivo. J. Immunol. Methods 293, 207–214 (2004).

    CAS  PubMed  Google Scholar 

  241. 241.

    Fridlender, Z. G. et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: “N1” versus “N2” TAN. Cancer Cell 16, 183–194 (2009). This article shows that TANs in lung cancer can polarize to either an N1 or an N2 phenotype that inhibits or promotes cancer development, respectively.

    CAS  PubMed  PubMed Central  Google Scholar 

  242. 242.

    Piccard, H., Muschel, R. J. & Opdenakker, G. On the dual roles and polarized phenotypes of neutrophils in tumor development and progression. Crit. Rev. Oncol. Hematol. 82, 296–309 (2012).

    CAS  PubMed  Google Scholar 

  243. 243.

    Grecian, R., Whyte, M. K. B. & Walmsley, S. R. The role of neutrophils in cancer. Br. Med. Bull. 128, 5–14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. 244.

    Injarabian, L., Devin, A., Ransac, S. & Marteyn, B. S. Neutrophil metabolic shift during their lifecycle: impact on their survival and activation. Int. J. Mol. Sci. 21, 287 (2019).

    PubMed Central  Google Scholar 

  245. 245.

    Jin, W. et al. Tumor-infiltrating NETs predict postsurgical survival in patients with pancreatic ductal adenocarcinoma. Ann. Surg. Oncol. 26, 635–643 (2019).

    PubMed  Google Scholar 

  246. 246.

    Pages, F. et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391, 2128–2139 (2018). This article validates the Immunoscore as a consensus and standardized cytotoxic T cell assay that defines immune hot, altered and cold tumours and finds that it has a greater prognostic value in CRC than T stage, N stage, lymphovascular invasion, tumour differentiation and MSI status.

    PubMed  Google Scholar 

  247. 247.

    Marliot, F. et al. Analytical validation of the Immunoscore and its associated prognostic value in patients with colon cancer. J. Immunother. Cancer 8, e000272 (2020).

    PubMed  PubMed Central  Google Scholar 

  248. 248.

    Marliot, F., Lafontaine, L. & Galon, J. Immunoscore assay for the immune classification of solid tumors: Technical aspects, improvements and clinical perspectives. Methods Enzymol. 636, 109–128 (2020).

    PubMed  Google Scholar 

  249. 249.

    Mlecnik, B. et al. Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J. Clin. Oncol. 29, 610–618 (2011).

    PubMed  Google Scholar 

  250. 250.

    Pages, F. et al. In situ cytotoxic and memory T cells predict outcome in patients with early-stage colorectal cancer. J. Clin. Oncol. 27, 5944–5951 (2009).

    CAS  PubMed  Google Scholar 

  251. 251.

    Angell, H. K., Bruni, D., Barrett, J. C., Herbst, R. & Galon, J. The immunoscore: colon cancer and beyond. Clin. Cancer Res. 26, 332–339 (2020).

    PubMed  Google Scholar 

  252. 252.

    Galon, J. & Lanzi, A. Immunoscore and its introduction in clinical practice. Q. J. Nucl. Med. Mol. Imaging 64, 152–161 (2020).

    PubMed  Google Scholar 

  253. 253.

    Mascaux, C. et al. Immune evasion before tumour invasion in early lung squamous carcinogenesis. Nature 571, 570–575 (2019). This article shows immune sensing in low-grade dysplasia and immune escape through immune checkpoints in high-grade preinvasive lung lesions.

    CAS  PubMed  Google Scholar 

  254. 254.

    Fridman, W. H. et al. Immune infiltration in human cancer: prognostic significance and disease control. Curr. Top. Microbiol. Immunol. 344, 1–24 (2011).

    CAS  PubMed  Google Scholar 

  255. 255.

    Galon, J. et al. Validation of the Immunoscore (IM) as a prognostic marker in stage I/II/III colon cancer: results of a worldwide consortium-based analysis of 1,336 patients. J. Clin. Oncol. 34, S3500 (2016).

    Google Scholar 

  256. 256.

    Kirilovsky, A. et al. Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int. Immunol. 28, 373–382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  257. 257.

    Van den Eynde, M. et al. The link between the multiverse of immune microenvironments in metastases and the survival of colorectal cancer patients. Cancer Cell 34, 1012–1026 e3 (2018).

    PubMed  Google Scholar 

  258. 258.

    Mlecnik, B. et al. Comprehensive intrametastatic immune quantification and major impact of Immunoscore on survival. J. Natl Cancer Inst. 110, 97–108 (2018).

    CAS  Google Scholar 

  259. 259.

    Pages, F. et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N. Engl. J. Med. 353, 2654–2666 (2005).

    CAS  PubMed  Google Scholar 

  260. 260.

    Mlecnik, B. et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci. Transl Med. 8, 327ra26 (2016).

    PubMed  Google Scholar 

  261. 261.

    Mlecnik, B. et al. Integrative analyses of colorectal cancer show Immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016).

    CAS  PubMed  Google Scholar 

  262. 262.

    Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765 e16 (2018). This study demonstrates that immune parameters, defined by the Immunoscore and immunoediting, in humans shaped the evolution of specific tumour clones.

    CAS  PubMed  Google Scholar 

  263. 263.

    Chew, V. et al. Chemokine-driven lymphocyte infiltration: an early intratumoural event determining long-term survival in resectable hepatocellular carcinoma. Gut 61, 427–438 (2012).

    CAS  PubMed  Google Scholar 

  264. 264.

    Gao, Q. et al. Tumor stroma reaction-related gene signature predicts clinical outcome in human hepatocellular carcinoma. Cancer Sci. 102, 1522–1531 (2011).

    CAS  PubMed  Google Scholar 

  265. 265.

    Harlin, H. et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 69, 3077–3085 (2009).

    CAS  PubMed  Google Scholar 

  266. 266.

    Mlecnik, B. et al. Biomolecular network reconstruction identifies T-cell homing factors associated with survival in colorectal cancer. Gastroenterology 138, 1429–1440 (2010).

    CAS  PubMed  Google Scholar 

  267. 267.

    Hendrickx, W. et al. Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. Oncoimmunology 6, e1253654 (2017).

    PubMed  PubMed Central  Google Scholar 

  268. 268.

    Foerster, F. et al. The immune contexture of hepatocellular carcinoma predicts clinical outcome. Sci. Rep. 8, 5351 (2018). Using deep sequencing of HCCs, this study finds differences in the immune contexture of HCCs, and that the presence of T cells and cytotoxic cells as well as the absence of macrophages and T H2 cells was positively correlated with patient survival.

    PubMed  PubMed Central  Google Scholar 

  269. 269.

    Mlecnik, B., Bindea, G., Pages, F. & Galon, J. Tumor immunosurveillance in human cancers. Cancer Metastasis Rev. 30, 5–12 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  270. 270.

    Galon, J. & Bruni, D. Tumor immunology and tumor evolution: intertwined histories. Immunity 52, 55–81 (2020).

    CAS  PubMed  Google Scholar 

  271. 271.

    Galon, J. & Bruni, D. The role of the immune infiltrate in distinct cancer types and its clinical implications: lymphocytic infiltration in colorectal cancer. Cancer Treat. Res. 180, 197–211 (2020).

    PubMed  Google Scholar 

  272. 272.

    Blank, C. U., Haanen, J. B., Ribas, A. & Schumacher, T. N. The “cancer immunogram”. Science 352, 658–660 (2016). This perspective presents a framework for analysing biomarkers predicting response to immunotherapy, summarized as a ‘cancer immunogram’.

    CAS  PubMed  Google Scholar 

  273. 273.

    Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019). This comprehensive review discusses the biomarkers predicting response to immunotherapy, including tumour genomics, host germline genetics, PDL1 levels, the microbiota and other features of the tumour microenvironment.

    CAS  PubMed  PubMed Central  Google Scholar 

  274. 274.

    Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  275. 275.

    Garassino, M. C. et al. Evaluation of blood TMB (bTMB) in KEYNOTE-189: pembrolizumab (pembro) plus chemotherapy (chemo) with pemetrexed and platinum versus placebo plus chemo as first-line therapy for metastatic nonsquamous NSCLC. J. Clin. Oncol. 38, 9521–9521 (2020).

    Google Scholar 

  276. 276.

    Langer, C. et al. Keynote-021: TMB and outcomes for carboplatin and pemetrexed with or without pembrolizumab for nonsquamous NSCLC. J. Thorac. Oncol. 14, S216 (2019).

    Google Scholar 

  277. 277.

    Chang, L., Chang, M., Chang, H. M. & Chang, F. Microsatellite instability: a predictive biomarker for cancer immunotherapy. Appl. Immunohistochem. Mol. Morphol. 26, e15–e21 (2018).

    CAS  PubMed  Google Scholar 

  278. 278.

    Galon, J. et al. The immune score as a new possible approach for the classification of cancer. J. Transl. Med. 10, 1 (2012).

    PubMed  PubMed Central  Google Scholar 

  279. 279.

    Galon, J. et al. Cancer classification using the Immunoscore: a worldwide task force. J. Transl. Med. 10, 205 (2012).

    PubMed  PubMed Central  Google Scholar 

  280. 280.

    Taube, J. M. et al. Implications of the tumor immune microenvironment for staging and therapeutics. Mod. Pathol. 31, 214–234 (2018).

    CAS  PubMed  Google Scholar 

  281. 281.

    Maleki Vareki, S., Garrigos, C. & Duran, I. Biomarkers of response to PD-1/PD-L1 inhibition. Crit. Rev. Oncol. Hematol. 116, 116–124 (2017).

    PubMed  Google Scholar 

  282. 282.

    Powles, T. et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 515, 558–562 (2014).

    CAS  PubMed  Google Scholar 

  283. 283.

    Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014). This article shows that the baseline density of T cells and their location at the invasive margin predicts treatment outcome of patients with metastatic melanoma receiving PD1-targeting therapies.

    CAS  PubMed  PubMed Central  Google Scholar 

  284. 284.

    Wimberly, H. et al. PD-L1 expression correlates with tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy in breast cancer. Cancer Immunol. Res. 3, 326–332 (2015).

    CAS  PubMed  Google Scholar 

  285. 285.

    Nowicki, T. S. et al. Infiltration of CD8 T cells and expression of PD-1 and PD-L1 in synovial sarcoma. Cancer Immunol. Res. 5, 118–126 (2017).

    CAS  PubMed  Google Scholar 

  286. 286.

    Ogura, A. et al. Pattern of programmed cell death-ligand 1 expression and CD8-positive T-cell infiltration before and after chemoradiotherapy in rectal cancer. Eur. J. Cancer 91, 11–20 (2018).

    CAS  PubMed  Google Scholar 

  287. 287.

    Subrahmanyam, P. B. et al. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J. Immunother. Cancer 6, 18 (2018).

    PubMed  PubMed Central  Google Scholar 

  288. 288.

    Tietze, J. K. et al. The proportion of circulating CD45RO+CD8+ memory T cells is correlated with clinical response in melanoma patients treated with ipilimumab. Eur. J. Cancer 75, 268–279 (2017).

    CAS  PubMed  Google Scholar 

  289. 289.

    Hiniker, S. M. et al. A prospective clinical trial combining radiation therapy with systemic immunotherapy in metastatic melanoma. Int. J. Radiat. Oncol. Biol. Phys. 96, 578–588 (2016).

    PubMed  PubMed Central  Google Scholar 

  290. 290.

    Montero, A. J. et al. Phase 2 study of neoadjuvant treatment with NOV-002 in combination with doxorubicin and cyclophosphamide followed by docetaxel in patients with HER-2 negative clinical stage II-IIIc breast cancer. Breast Cancer Res. Treat. 132, 215–223 (2012).

    CAS  PubMed  Google Scholar 

  291. 291.

    Kaufman, H. L. et al. Local and distant immunity induced by intralesional vaccination with an oncolytic herpes virus encoding GM-CSF in patients with stage IIIc and IV melanoma. Ann. Surg. Oncol. 17, 718–730 (2010).

    PubMed  Google Scholar 

  292. 292.

    Romano, A. et al. Circulating myeloid-derived suppressor cells correlate with clinical outcome in Hodgkin Lymphoma patients treated up-front with a risk-adapted strategy. Br. J. Haematol. 168, 689–700 (2015).

    CAS  PubMed  Google Scholar 

  293. 293.

    Stoll, G. et al. Immune-related gene signatures predict the outcome of neoadjuvant chemotherapy. Oncoimmunology 3, e27884 (2014).

    PubMed  PubMed Central  Google Scholar 

  294. 294.

    Wei, Y. et al. CXCL13 expression is prognostic and predictive for postoperative adjuvant chemotherapy benefit in patients with gastric cancer. Cancer Immunol. Immunother. 67, 261–269 (2018).

    CAS  PubMed  Google Scholar 

  295. 295.

    Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018). This study demonstrates that PD1 hiCD8 + T cells secreting CXCL13 are predominantly located in TLSs and predict response to anti-PD1 therapy.

    CAS  PubMed  PubMed Central  Google Scholar 

  296. 296.

    Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  297. 297.

    Berinstein, N. L. et al. Increased lymphocyte infiltration in patients with head and neck cancer treated with the IRX-2 immunotherapy regimen. Cancer Immunol. Immunother. 61, 771–782 (2012).

    CAS  PubMed  Google Scholar 

  298. 298.

    Helmink, B. A. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).

    CAS  PubMed  Google Scholar 

  299. 299.

    Bindea, G., Mlecnik, B., Angell, H. K. & Galon, J. The immune landscape of human tumors: Implications for cancer immunotherapy. Oncoimmunology 3, e27456 (2014).

    PubMed  PubMed Central  Google Scholar 

  300. 300.

    Bao, Y. et al. Identification of IFN-gamma-producing innate B cells. Cell Res. 24, 161–176 (2014).

    CAS  PubMed  Google Scholar 

  301. 301.

    Jahrsdorfer, B. et al. B-chronic lymphocytic leukemia cells and other B cells can produce granzyme B and gain cytotoxic potential after interleukin-21-based activation. Blood 108, 2712–2719 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  302. 302.

    Hagn, M. et al. Human B cells differentiate into granzyme B-secreting cytotoxic B lymphocytes upon incomplete T-cell help. Immunol. Cell Biol. 90, 457–467 (2012).

    CAS  PubMed  Google Scholar 

  303. 303.

    Das, R. et al. Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J. Clin. Invest. 128, 715–720 (2018).

    PubMed  PubMed Central  Google Scholar 

  304. 304.

    Liudahl, S. M. & Coussens, L. M. B cells as biomarkers: predicting immune checkpoint therapy adverse events. J. Clin. Invest. 128, 577–579 (2018).

    PubMed  PubMed Central  Google Scholar 

  305. 305.

    Bindea, G., Mlecnik, B., Fridman, W. H. & Galon, J. The prognostic impact of anti-cancer immune response: a novel classification of cancer patients. Semin. Immunopathol. 33, 335–340 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  306. 306.

    Church, S. E. & Galon, J. Tumor microenvironment and immunotherapy: the whole picture is better than a glimpse. Immunity 43, 631–633 (2015).

    CAS  PubMed  Google Scholar 

  307. 307.

    Galon, J. et al. Immunoscore and immunoprofiling in cancer: an update from the melanoma and immunotherapy bridge 2015. J. Transl. Med. 14, 273 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  308. 308.

    Gnjatic, S. et al. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J. Immunother. Cancer 5, 44 (2017).

    PubMed  PubMed Central  Google Scholar 

  309. 309.

    Pages, F., Galon, J. & Fridman, W. H. The essential role of the in situ immune reaction in human colorectal cancer. J. Leukoc. Biol. 84, 981–987 (2008).

    CAS  PubMed  Google Scholar 

  310. 310.

    DeNardo, D. G. et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov. 1, 54–67 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  311. 311.

    Mony, J. T. & Schuchert, M. J. Prognostic implications of heterogeneity in intra-tumoral immune composition for recurrence in early stage lung cancer. Front. Immunol. 9, 2298 (2018).

    PubMed  PubMed Central  Google Scholar 

  312. 312.

    Wang, J. H. et al. Combined prognostic value of the cancer stem cell markers CD47 and CD133 in esophageal squamous cell carcinoma. Cancer Med. 8, 1315–1325 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  313. 313.

    Maby, P., Corneau, A. & Galon, J. Phenotyping of tumor infiltrating immune cells using mass-cytometry (CyTOF). Methods Enzymol. 632, 339–368 (2020).

    PubMed  Google Scholar 

  314. 314.

    Vasaturo, A. & Galon, J. Multiplexed immunohistochemistry for immune cell phenotyping, quantification and spatial distribution in situ. Methods Enzymol. 635, 51–66 (2020).

    PubMed  Google Scholar 

  315. 315.

    Giraldo, N. A. et al. Multidimensional, quantitative assessment of PD-1/PD-L1 expression in patients with Merkel cell carcinoma and association with response to pembrolizumab. J. Immunother. Cancer 6, 99 (2018).

    PubMed  PubMed Central  Google Scholar 

  316. 316.

    Althammer, S. et al. Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy. J. Immunother. Cancer 7, 121 (2019).

    PubMed  PubMed Central  Google Scholar 

  317. 317.

    Bedognetti, D., Hendrickx, W., Marincola, F. M. & Miller, L. D. Prognostic and predictive immune gene signatures in breast cancer. Curr. Opin. Oncol. 27, 433–444 (2015).

    CAS  PubMed  Google Scholar 

  318. 318.

    Nirmal, A. J. et al. Immune cell gene signatures for profiling the microenvironment of solid tumors. Cancer Immunol. Res. 6, 1388–1400 (2018).

    CAS  PubMed  Google Scholar 

  319. 319.

    Givechian, K. B. et al. Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples. NPJ Genom. Med. 3, 14 (2018).

    PubMed  PubMed Central  Google Scholar 

  320. 320.

    Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  321. 321.

    Kim, J. M. & Chen, D. S. Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure). Ann. Oncol. 27, 1492–1504 (2016).

    CAS  PubMed  Google Scholar 

  322. 322.

    Khaznadar, Z. et al. Defective NK cells in acute myeloid leukemia patients at diagnosis are associated with blast transcriptional signatures of immune evasion. J. Immunol. 195, 2580–2590 (2015).

    CAS  PubMed  Google Scholar 

  323. 323.

    Chen, H. Y. et al. Inhibiting the CD8+ T cell infiltration in the tumor microenvironment after radiotherapy is an important mechanism of radioresistance. Sci. Rep. 8, 11934 (2018). This study shows in mouse models and patients a novel mechanism of radioresistance, with tumour cells inhibiting the infiltration of CD8 + T cells after radiotherapy and becoming radioresistant.

    PubMed  PubMed Central  Google Scholar 

  324. 324.

    Galon, J. et al. Characterization of anti-CD19 chimeric antigen receptor (CAR) T cell-mediated tumor microenvironment immune gene profile in a multicenter trial (ZUMA-1) with axicabtagene ciloleucel (axi-cel, KTE-C19). J. Clin. Oncol.35 (Suppl. 15), 3025 (2017).

    Google Scholar 

  325. 325.

    Chow, M. T. et al. Intratumoral activity of the CXCR3 chemokine system is required for the efficacy of anti-PD-1 therapy. Immunity 50, 1498–1512 e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  326. 326.

    Buque, A. et al. Trial watch: immunomodulatory monoclonal antibodies for oncological indications. Oncoimmunology 4, e1008814 (2015).

    PubMed  PubMed Central  Google Scholar 

  327. 327.

    Galluzzi, L. et al. Trial watch: adoptive cell transfer immunotherapy. Oncoimmunology 1, 306–315 (2012).

    PubMed  PubMed Central  Google Scholar 

  328. 328.

    Pol, J. et al. Trial watch: peptide-based anticancer vaccines. Oncoimmunology 4, e974411 (2015).

    PubMed  PubMed Central  Google Scholar 

  329. 329.

    Senovilla, L. et al. Trial watch: DNA vaccines for cancer therapy. Oncoimmunology 2, e23803 (2013).

    PubMed  PubMed Central  Google Scholar 

  330. 330.

    Vacchelli, E. et al. Trial watch: monoclonal antibodies in cancer therapy. Oncoimmunology 2, e22789 (2013).

    PubMed  PubMed Central  Google Scholar 

  331. 331.

    Vacchelli, E. et al. Trial watch: immunostimulatory cytokines. Oncoimmunology 1, 493–506 (2012).

    PubMed  PubMed Central  Google Scholar 

  332. 332.

    Vacchelli, E. et al. Trial watch: chemotherapy with immunogenic cell death inducers. Oncoimmunology 1, 179–188 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  333. 333.

    Vacchelli, E. et al. Trial watch: peptide vaccines in cancer therapy. Oncoimmunology 1, 1557–1576 (2012).

    PubMed  PubMed Central  Google Scholar 

  334. 334.

    Sinicrope, F. A. et al. Contribution of immunoscore and molecular features to survival prediction in stage III colon cancer. JNCI Cancer Spectr. 4, pkaa023 (2020).

    PubMed  PubMed Central  Google Scholar 

  335. 335.

    Pagès, F. et al. Prognostic and predictive value of the Immunoscore in stage III colon cancer patients treated with oxaliplatin in the prospective IDEA France Prodige-Gercor cohort study. Ann. Oncol. 31, 921–929 (2020). This randomized phase III clinical trial demonstrates the predictive value of the Immunoscore in response to chemotherapy, and illustrated the importance of pre-existing adaptive immunity for the effectiveness of chemotherapy.

    PubMed  Google Scholar 

  336. 336.

    Zom, G. G. et al. Novel TLR2-binding adjuvant induces enhanced T cell responses and tumor eradication. J. Immunother. Cancer 6, 146 (2018).

    PubMed  PubMed Central  Google Scholar 

  337. 337.

    Lanitis, E., Dangaj, D., Irving, M. & Coukos, G. Mechanisms regulating T-cell infiltration and activity in solid tumors. Ann. Oncol. 28, xii18–xii32 (2017).

    CAS  PubMed  Google Scholar 

  338. 338.

    Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016). In this study, acquired resistance to PD1 blockade in patients with melanoma is associated with defects in the pathways involved in interferon-receptor signalling (JAK1 and JAK2) and in antigen presentation (B2M).

    CAS  PubMed  PubMed Central  Google Scholar 

  339. 339.

    Marabelle, A., Aspeslagh, S., Postel-Vinay, S. & Soria, J. C. JAK mutations as escape mechanisms to anti-PD-1 therapy. Cancer Discov. 7, 128–130 (2017).

    CAS  PubMed  Google Scholar 

  340. 340.

    Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).

    CAS  PubMed  Google Scholar 

  341. 341.

    Bazzichetto, C. et al. PTEN as a prognostic/predictive biomarker in cancer: an unfulfilled promise? Cancers 11, 435 (2019).

    CAS  PubMed Central  Google Scholar 

  342. 342.

    Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502 e15 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  343. 343.

    Quezada-Marin, J. et al. Gastrointestinal tissue-based molecular biomarkers: a practical categorization based on the 2019 WHO classification of epithelial digestive tumours. Histopathology https://doi.org/10.1111/his.14120 (2020). This work aims to organize and update gastrointestinal molecular biomarkers in order to produce an easy-to-use guide for routine diagnostics. It introduces the immune response as measured by the Immunoscore as an essential and desirable parameter for the classification of gastrointestinal tumours.

    Article  PubMed  Google Scholar 

  344. 344.

    Oliveira, L. J. C., Gongora, A. B. L. & Jardim, D. L. F. Spectrum and clinical activity of PD-1/PD-L1 inhibitors: regulatory approval and under development. Curr. Oncol. Rep. 22, 70 (2020).

    PubMed  Google Scholar 

  345. 345.

    Ohta, M. et al. The high expression of fractalkine results in a better prognosis for colorectal cancer patients. Int. J. Oncol. 26, 41–47 (2005).

    CAS  PubMed  Google Scholar 

  346. 346.

    Ding, Q. et al. CXCL9: evidence and contradictions for its role in tumor progression. Cancer Med. 5, 3246–3259 (2016).

    PubMed  PubMed Central  Google Scholar 

  347. 347.

    Bronger, H. et al. CXCL9 and CXCL10 predict survival and are regulated by cyclooxygenase inhibition in advanced serous ovarian cancer. Br. J. Cancer 115, 553–563 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  348. 348.

    de Chaisemartin, L. et al. Characterization of chemokines and adhesion molecules associated with T cell presence in tertiary lymphoid structures in human lung cancer. Cancer Res. 71, 6391–6399 (2011).

    PubMed  Google Scholar 

  349. 349.

    Crotty, S. T follicular helper cell differentiation, function, and roles in disease. Immunity 41, 529–542 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  350. 350.

    Luther, S. A., Lopez, T., Bai, W., Hanahan, D. & Cyster, J. G. BLC expression in pancreatic islets causes B cell recruitment and lymphotoxin-dependent lymphoid neogenesis. Immunity 12, 471–481 (2000).

    CAS  PubMed  Google Scholar 

  351. 351.

    Kim, C. H. et al. Unique gene expression program of human germinal center T helper cells. Blood 104, 1952–1960 (2004).

    CAS  PubMed  Google Scholar 

  352. 352.

    Kroenke, M. A. et al. Bcl6 and Maf cooperate to instruct human follicular helper CD4 T cell differentiation. J. Immunol. 188, 3734–3744 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  353. 353.

    Razis, E. et al. Improved outcome of high-risk early HER2 positive breast cancer with high CXCL13-CXCR5 messenger RNA expression. Clin. Breast Cancer 12, 183–193 (2012).

    CAS  PubMed  Google Scholar 

  354. 354.

    Chen, L. et al. The expression of CXCL13 and its relation to unfavorable clinical characteristics in young breast cancer. J. Transl. Med. 13, 168 (2015).

    PubMed  PubMed Central  Google Scholar 

  355. 355.

    Biswas, S. et al. CXCL13-CXCR5 co-expression regulates epithelial to mesenchymal transition of breast cancer cells during lymph node metastasis. Breast Cancer Res. Treat. 143, 265–276 (2014).

    CAS  PubMed  Google Scholar 

  356. 356.

    Xu, X., Xia, J. & Wang, X. Potential anticancer therapies via CXCL5 and its receptors. Expert Rev. Clin. Pharmacol. 5, 347–350 (2012).

    CAS  PubMed  Google Scholar 

  357. 357.

    Hu, B., Fan, H., Lv, X., Chen, S. & Shao, Z. Prognostic significance of CXCL5 expression in cancer patients: a meta-analysis. Cancer Cell Int. 18, 68 (2018).

    PubMed  PubMed Central  Google Scholar 

  358. 358.

    Kowalczuk, O. et al. CXCL5 as a potential novel prognostic factor in early stage non-small cell lung cancer: results of a study of expression levels of 23 genes. Tumour Biol. 35, 4619–4628 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  359. 359.

    Zhou, S. L. et al. CXCL5 contributes to tumor metastasis and recurrence of intrahepatic cholangiocarcinoma by recruiting infiltrative intratumoral neutrophils. Carcinogenesis 35, 597–605 (2014).

    CAS  PubMed  Google Scholar 

  360. 360.

    Zhu, X. et al. CXCL5 is a potential diagnostic and prognostic marker for bladder cancer patients. Tumour Biol. 37, 4569–4577 (2016).

    CAS  PubMed  Google Scholar 

  361. 361.

    Li, A. et al. Overexpression of CXCL5 is associated with poor survival in patients with pancreatic cancer. Am. J. Pathol. 178, 1340–1349 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  362. 362.

    Zitvogel, L., Kepp, O. & Kroemer, G. Immune parameters affecting the efficacy of chemotherapeutic regimens. Nat. Rev. Clin. Oncol. 8, 151–160 (2011).

    CAS  PubMed  Google Scholar 

  363. 363.

    Gandhi, L. et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N. Engl. J. Med. 378, 2078–2092 (2018). This study finds that the addition of anti-PD1 (pembrolizumab) to standard chemotherapy resulted in significantly longer OS and PFS than chemotherapy alone.

    CAS  PubMed  Google Scholar 

  364. 364.

    Brea, E. J. et al. Kinase regulation of human MHC class I molecule expression on cancer cells. Cancer Immunol. Res. 4, 936–947 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  365. 365.

    Wilmott, J. S. et al. Selective BRAF inhibitors induce marked T-cell infiltration into human metastatic melanoma. Clin. Cancer Res. 18, 1386–1394 (2012).

    CAS  PubMed  Google Scholar 

  366. 366.

    Adotevi, O. et al. A decrease of regulatory T cells correlates with overall survival after sunitinib-based antiangiogenic therapy in metastatic renal cancer patients. J. Immunother. 33, 991–998 (2010).

    CAS  PubMed  Google Scholar 

  367. 367.

    Yang, J. C. et al. A randomized trial of bevacizumab, an anti-vascular endothelial growth factor antibody, for metastatic renal cancer. N. Engl. J. Med. 349, 427–434 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  368. 368.

    Barker, H. E., Paget, J. T., Khan, A. A. & Harrington, K. J. The tumour microenvironment after radiotherapy: mechanisms of resistance and recurrence. Nat. Rev. Cancer 15, 409–425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  369. 369.

    Formenti, S. C. & Demaria, S. Combining radiotherapy and cancer immunotherapy: a paradigm shift. J. Natl Cancer Inst. 105, 256–265 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  370. 370.

    Reits, E. A. et al. Radiation modulates the peptide repertoire, enhances MHC class I expression, and induces successful antitumor immunotherapy. J. Exp. Med. 203, 1259–1271 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  371. 371.

    Vanpouille-Box, C. et al. DNA exonuclease Trex1 regulates radiotherapy-induced tumour immunogenicity. Nat. Commun. 8, 15618 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the French National Cancer Institute (INCa), Cancéropôle Île de France, INSERM, AstraZeneca, the Transcan ERAnet European Project, La Ligue contre le Cancer, the Qatar National Research Fund, Cancer Research for Personalized Medicine (CARPEM), the Paris Alliance of Cancer Research Institutes and LabEx Immuno-oncology.

Author information

Affiliations

Authors

Contributions

All authors researched data for the article, made substantial contributions to discussion of content and wrote, reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Jérôme Galon.

Ethics declarations

Competing interests

J.G. is a co-founder of HalioDx. H.K.A. is an employee of AstraZeneca. D.B. is now an employee of Roche Diagnostics International, but the work on this review was done when employed by INSERM.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Immune contexture

A concept that encompasses the combination of immune parameters linked with patients’ survival associating the type, density, immune functional orientation and localization of immune cells within a tumour. ‘Contexture’ refers to the act of assembling parts into a whole, and an arrangement of interconnected parts.

CD3+ T cells

T cells can be distinguished from other lymphocytes by the presence of a T cell receptor and CD3 on the cell surface of all T cells.

Humoral adaptive immunity

Immunity mediated by macromolecules found in extracellular fluids such as secreted antibodies, complement proteins and certain antimicrobial peptides and that involves the activation of B cells.

Granule

A cytoplasmic structure found in mast cells that functions as storage for preformed mediators, which are promptly released by exocytosis on stimulation.

Neutrophil extracellular traps

(NETs). Networks of extracellular fibres, primarily composed of DNA from neutrophils, which bind pathogens.

Effector memory T cells

Cells whose primary function is to augment an immune response when reactivated. These lymphocytes are primarily active as the CD8 variants, and thus are mainly responsible for cytotoxic actions against pathogens.

CD8+ central memory T cells

Cells that are CD62LhiCCR7hi; CD62L and CCR7 facilitate homing of these cells to secondary lymphoid organs. They provide central immunosurveillance against known pathogens and can give rise to both effector and effector memory T cells following antigen re-encounter. They can also localize to peripheral tissues, where they participate in primary immunosurveillance.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bruni, D., Angell, H.K. & Galon, J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer (2020). https://doi.org/10.1038/s41568-020-0285-7

Download citation

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing