FLOW CYTOMETRIC EVALUATION OF PERIPHERAL BLOOD BIOMARKERS FOR SOLID TUMOURS IMMUNOTHERAPY GUIDING: A REVIEW
Abstract
Cancer treatment is an area that is constantly being updated, namely with the discovery of immunotherapy as a clinically effective therapeutic modality for various types of cancer. Among these innovative therapies, immune checkpoint inhibitors (ICI) have proven to provide significant and long-lasting responses. However, this response does not occur for all patients, i.e., some patients do not benefit from this therapy. Due to this heterogeneity in immunotherapy response, there is an urgent need to identify and establish biomarkers that will allow the identification of patients who will respond to the therapy, while sparing the non-responders from adverse effects. In addition, the use these biomarkers in monitoring the response during treatment seems promising. Given the recognized role of the immune system in the anti-tumour response, these cells have been intensively studied as potential biomarkers. Their study in peripheral blood (PB) has been of great interest and importance, given its easy accessibility and less invasive nature. The detailed and integral evaluation of peripheral immunity requires a multiparametric methodology such as flow cytometry (FC), applying the simultaneous analysis of lineage markers together with maturation, activation and functional state markers. In this narrative literature review, we intend to describe the “state of the art” on the FC study of PB immune cell populations as potential biomarkers for ICI therapy in solid tumours. The results found are presented for each of the major populations and their subsets, namely T lymphocytes, myeloid-derived suppressor cells (MDSCs), neutrophils, eosinophils, dendritic cells (CD), natural killer cells (NK), monocyte subsets, and B cells.
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References
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26. Lepone, L.M., et al., Analyses of 123 Peripheral Human Immune Cell Subsets: Defining Differences with Age and between Healthy Donors and Cancer Patients Not Detected in Analysis of Standard Immune Cell Types. J Circ Biomark, 2016. 5: p. 5.
27. Felix, J., et al., Ipilimumab reshapes T cell memory subsets in melanoma patients with clinical response. Oncoimmunology, 2016. 5(7): p. 1136045.
28. Wistuba-Hamprecht, K., et al., Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur J Cancer, 2017. 73: p. 61-70.
29. Wang, W., et al., Biomarkers on melanoma patient T cells associated with ipilimumab treatment. J Transl Med, 2012. 10: p. 146.
30. Ku, G.Y., et al., Single-institution experience with ipilimumab in advanced melanoma patients in the compassionate use setting: lymphocyte count after 2 doses correlates with survival. Cancer, 2010. 116(7): p. 1767-75.
31. Peranzoni, E., et al., Myeloid Cells as Clinical Biomarkers for Immune Checkpoint Blockade. Front Immunol, 2020. 11: p. 1590.
32. Kotsakis, A., et al., Myeloid-derived suppressor cell measurements in fresh and cryopreserved blood samples. J Immunol Methods, 2012. 381(1-2): p. 14-22.
33. Apodaca, M.C., et al., Characterization of a whole blood assay for quantifying myeloid-derived suppressor cells. J Immunother Cancer, 2019. 7(1): p. 230.
34. Bronte, V., et al., Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun, 2016. 7: p. 12150.
35. Meyer, C., et al., Frequencies of circulating MDSC correlate with clinical outcome of melanoma patients treated with ipilimumab. Cancer Immunol Immunother, 2014. 63(3): p. 247-57.
36. Sade-Feldman, M., et al., Clinical Significance of Circulating CD33+CD11b+HLA-DR- Myeloid Cells in Patients with Stage IV Melanoma Treated with Ipilimumab. Clin Cancer Res, 2016. 22(23): p. 5661-5672.
37. Pico de Coana, Y., et al., Ipilimumab treatment results in an early decrease in the frequency of circulating granulocytic myeloid-derived suppressor cells as well as their Arginase1 production. Cancer Immunol Res, 2013. 1(3): p. 158-62.
38. Poschke, I., et al., Immature immunosuppressive CD14+HLA-DR-/low cells in melanoma patients are Stat3hi and overexpress CD80, CD83, and DC-sign. Cancer Res, 2010. 70(11): p. 4335-45.
39. Limagne, E., et al., Tim-3/galectin-9 pathway and mMDSC control primary and secondary resistances to PD-1 blockade in lung cancer patients. Oncoimmunology, 2019. 8(4): p. e1564505.
40. Li, Y., et al., Pretreatment Neutrophil-to-Lymphocyte Ratio (NLR) May Predict the Outcomes of Advanced Non-small-cell Lung Cancer (NSCLC) Patients Treated With Immune Checkpoint Inhibitors (ICIs). Front Oncol, 2020. 10: p. 654.
41. Moller, M., et al., Blood Immune Cell Biomarkers in Patient With Lung Cancer Undergoing Treatment With Checkpoint Blockade. J Immunother, 2020. 43(2): p. 57-66.
42. Simon, S.C.S., et al., Eosinophil accumulation predicts response to melanoma treatment with immune checkpoint inhibitors. Oncoimmunology, 2020. 9(1): p. 1727116.
43. Gebhardt, C., et al., Myeloid Cells and Related Chronic Inflammatory Factors as Novel Predictive Markers in Melanoma Treatment with Ipilimumab. Clin Cancer Res, 2015. 21(24): p. 5453-9.
44. Carretero, R., et al., Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(+) T cells. Nat Immunol, 2015. 16(6): p. 609-17.
45. Splunter, M.V., et al., Plasmacytoid dendritic cell and myeloid dendritic cell function in ageing: A comparison between elderly and young adult women. PLoS One, 2019. 14(12): p. e0225825.
46. Paul, S. and G. Lal, The Molecular Mechanism of Natural Killer Cells Function and Its Importance in Cancer Immunotherapy. Front Immunol, 2017. 8: p. 1124.
47. Krijgsman, D., et al., Characterization of circulating T-, NK-, and NKT cell subsets in patients with colorectal cancer: the peripheral blood immune cell profile. Cancer Immunol Immunother, 2019. 68(6): p. 1011-1024.
48. Cao, Y., et al., Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy. Signal Transduct Target Ther, 2020. 5(1): p. 250.
49. Tang, B., et al., Natural killer cells as a predictive biomarker for response to anti-PD-1 therapy in patients with advanced solid tumors. Journal of Clinical Oncology, 2017. 35: p. e21055-e21055.
50. Youn, J.I., et al., Peripheral natural killer cells and myeloid-derived suppressor cells correlate with anti-PD-1 responses in non-small cell lung cancer. Sci Rep, 2020. 10(1): p. 9050.
51. Holl, E.K., et al., Examining Peripheral and Tumor Cellular Immunome in Patients With Cancer. Front Immunol, 2019. 10: p. 1767.
52. Cloughesy, T.F., et al., Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med, 2019. 25(3): p. 477-486.
53. Krieg, C., et al., High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med, 2018. 24(2): p. 144-153.
54. Wennhold, K., A. Shimabukuro-Vornhagen, and M. von Bergwelt-Baildon, B Cell-Based Cancer Immunotherapy. Transfus Med Hemother, 2019. 46(1): p. 36-46.
55. Das, R., et al., Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J Clin Invest, 2018.128(2): p. 715-720.
2. Spencer, K.R., et al., Biomarkers for Immunotherapy: Current Developments and Challenges. Am Soc Clin Oncol Educ Book, 2016. 35: p. e493-503.
3. Voutsadakis, I.A., Prediction of Immune checkpoint inhibitors benefit from routinely measurable peripheral blood parameters. Chin Clin Oncol, 2020. 9(2): p. 19.
4. Li, S., et al., Emerging Blood-Based Biomarkers for Predicting Response to Checkpoint Immunotherapy in Non-Small-Cell Lung Cancer. Front Immunol, 2020. 11: p. 603157.
5. Rotte, A., J.Y. Jin, and V. Lemaire, Mechanistic overview of immune checkpoints to support the rational design of their combinations in cancer immunotherapy. Ann Oncol, 2018. 29(1): p. 71-83.
6. Zhang, M., et al., Monitoring checkpoint inhibitors: predictive biomarkers in immunotherapy. Front Med, 2019. 13(1): p. 32-44.
7. Quandt, D., et al., Implementing liquid biopsies into clinical decision making for cancer immunotherapy. Oncotarget, 2017. 8(29): p. 48507-48520.
8. Guo, L., et al., Colorectal Cancer Immune Infiltrates: Significance in Patient Prognosis and Immunotherapeutic Efficacy. Front Immunol, 2020. 11: p. 1052.
9. Galon, J., et al., Immunoscore and Immunoprofiling in cancer: an update from the melanoma and immunotherapy bridge 2015. J Transl Med, 2016. 14: p. 273.
10. Hernandez, C., et al., Systemic Blood Immune Cell Populations as Biomarkers for the Outcome of Immune Checkpoint Inhibitor Therapies. Int J Mol Sci, 2020. 21(7).
11. Schnell, A., et al., The Peripheral and Intratumoral Immune Cell Landscape in Cancer Patients: A Proxy for Tumor Biology and a Tool for Outcome Prediction. Biomedicines, 2018. 6(1).
12. Martens, A., et al., Baseline Peripheral Blood Biomarkers Associated with Clinical Outcome of Advanced Melanoma Patients Treated with Ipilimumab. Clin Cancer Res, 2016. 22(12): p. 2908-18.
13. George, A.P., et al., The Discovery of Biomarkers in Cancer Immunotherapy. Comput Struct Biotechnol J, 2019. 17: p. 484-497.
14. Whiteside, T.L., Immune responses to cancer: are they potential biomarkers of prognosis? Front Oncol, 2013. 3: p. 107.
15. Riemann, D., et al., Blood immune cell biomarkers in lung cancer. Clin Exp Immunol, 2019. 195(2): p. 179-189.
16. Pardoll, D.M., The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer, 2012. 12(4): p. 252-64.
17. Nishino, M., et al., Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol, 2017. 14(11): p. 655-668.
18. Jochems, C., et al., A combination trial of vaccine plus ipilimumab in metastatic castration-resistant prostate cancer patients: immune correlates. Cancer Immunol Immunother, 2014. 63(4): p. 407-18.
19. Delyon, J., et al., Experience in daily practice with ipilimumab for the treatment of patients with metastatic melanoma: an early increase in lymphocyte and eosinophil counts is associated with improved survival. Ann Oncol, 2013. 24(6): p. 1697-703.
20. Tarhini, A.A., et al., Immune monitoring of the circulation and the tumor microenvironment in patients with regionally advanced melanoma receiving neoadjuvant ipilimumab. PLoS One, 2014. 9(2): p. e87705.
21. Simeone, E., et al., Immunological and biological changes during ipilimumab treatment and their potential correlation with clinical response and survival in patients with advanced melanoma. Cancer Immunol Immunother, 2014. 63(7): p. 675-83.
22. Hotson, D., et al., CTLA-4 Defines Distinct T Cell Signaling Populations in Healthy Donors and Metastatic Melanoma Patients. 2012. 760-760.
23. Retseck, J., et al., Phenotypic and functional testing of circulating regulatory T cells in advanced melanoma patients treated with neoadjuvant ipilimumab. J Immunother Cancer, 2016. 4: p. 38.
24. Martens, A., et al., Increases in Absolute Lymphocytes and Circulating CD4+ and CD8+ T Cells Are Associated with Positive Clinical Outcome of Melanoma Patients Treated with Ipilimumab. Clin Cancer Res, 2016. 22(19): p. 4848-4858.
25. Pico de Coaña, Y., et al., Ipilimumab treatment decreases circulating Tregs and GrMDSC while enhancing CD4+ T cell activation. Journal of Translational Medicine, 2015. 13(Suppl 1): p. O7-O7.
26. Lepone, L.M., et al., Analyses of 123 Peripheral Human Immune Cell Subsets: Defining Differences with Age and between Healthy Donors and Cancer Patients Not Detected in Analysis of Standard Immune Cell Types. J Circ Biomark, 2016. 5: p. 5.
27. Felix, J., et al., Ipilimumab reshapes T cell memory subsets in melanoma patients with clinical response. Oncoimmunology, 2016. 5(7): p. 1136045.
28. Wistuba-Hamprecht, K., et al., Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur J Cancer, 2017. 73: p. 61-70.
29. Wang, W., et al., Biomarkers on melanoma patient T cells associated with ipilimumab treatment. J Transl Med, 2012. 10: p. 146.
30. Ku, G.Y., et al., Single-institution experience with ipilimumab in advanced melanoma patients in the compassionate use setting: lymphocyte count after 2 doses correlates with survival. Cancer, 2010. 116(7): p. 1767-75.
31. Peranzoni, E., et al., Myeloid Cells as Clinical Biomarkers for Immune Checkpoint Blockade. Front Immunol, 2020. 11: p. 1590.
32. Kotsakis, A., et al., Myeloid-derived suppressor cell measurements in fresh and cryopreserved blood samples. J Immunol Methods, 2012. 381(1-2): p. 14-22.
33. Apodaca, M.C., et al., Characterization of a whole blood assay for quantifying myeloid-derived suppressor cells. J Immunother Cancer, 2019. 7(1): p. 230.
34. Bronte, V., et al., Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun, 2016. 7: p. 12150.
35. Meyer, C., et al., Frequencies of circulating MDSC correlate with clinical outcome of melanoma patients treated with ipilimumab. Cancer Immunol Immunother, 2014. 63(3): p. 247-57.
36. Sade-Feldman, M., et al., Clinical Significance of Circulating CD33+CD11b+HLA-DR- Myeloid Cells in Patients with Stage IV Melanoma Treated with Ipilimumab. Clin Cancer Res, 2016. 22(23): p. 5661-5672.
37. Pico de Coana, Y., et al., Ipilimumab treatment results in an early decrease in the frequency of circulating granulocytic myeloid-derived suppressor cells as well as their Arginase1 production. Cancer Immunol Res, 2013. 1(3): p. 158-62.
38. Poschke, I., et al., Immature immunosuppressive CD14+HLA-DR-/low cells in melanoma patients are Stat3hi and overexpress CD80, CD83, and DC-sign. Cancer Res, 2010. 70(11): p. 4335-45.
39. Limagne, E., et al., Tim-3/galectin-9 pathway and mMDSC control primary and secondary resistances to PD-1 blockade in lung cancer patients. Oncoimmunology, 2019. 8(4): p. e1564505.
40. Li, Y., et al., Pretreatment Neutrophil-to-Lymphocyte Ratio (NLR) May Predict the Outcomes of Advanced Non-small-cell Lung Cancer (NSCLC) Patients Treated With Immune Checkpoint Inhibitors (ICIs). Front Oncol, 2020. 10: p. 654.
41. Moller, M., et al., Blood Immune Cell Biomarkers in Patient With Lung Cancer Undergoing Treatment With Checkpoint Blockade. J Immunother, 2020. 43(2): p. 57-66.
42. Simon, S.C.S., et al., Eosinophil accumulation predicts response to melanoma treatment with immune checkpoint inhibitors. Oncoimmunology, 2020. 9(1): p. 1727116.
43. Gebhardt, C., et al., Myeloid Cells and Related Chronic Inflammatory Factors as Novel Predictive Markers in Melanoma Treatment with Ipilimumab. Clin Cancer Res, 2015. 21(24): p. 5453-9.
44. Carretero, R., et al., Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(+) T cells. Nat Immunol, 2015. 16(6): p. 609-17.
45. Splunter, M.V., et al., Plasmacytoid dendritic cell and myeloid dendritic cell function in ageing: A comparison between elderly and young adult women. PLoS One, 2019. 14(12): p. e0225825.
46. Paul, S. and G. Lal, The Molecular Mechanism of Natural Killer Cells Function and Its Importance in Cancer Immunotherapy. Front Immunol, 2017. 8: p. 1124.
47. Krijgsman, D., et al., Characterization of circulating T-, NK-, and NKT cell subsets in patients with colorectal cancer: the peripheral blood immune cell profile. Cancer Immunol Immunother, 2019. 68(6): p. 1011-1024.
48. Cao, Y., et al., Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy. Signal Transduct Target Ther, 2020. 5(1): p. 250.
49. Tang, B., et al., Natural killer cells as a predictive biomarker for response to anti-PD-1 therapy in patients with advanced solid tumors. Journal of Clinical Oncology, 2017. 35: p. e21055-e21055.
50. Youn, J.I., et al., Peripheral natural killer cells and myeloid-derived suppressor cells correlate with anti-PD-1 responses in non-small cell lung cancer. Sci Rep, 2020. 10(1): p. 9050.
51. Holl, E.K., et al., Examining Peripheral and Tumor Cellular Immunome in Patients With Cancer. Front Immunol, 2019. 10: p. 1767.
52. Cloughesy, T.F., et al., Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med, 2019. 25(3): p. 477-486.
53. Krieg, C., et al., High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med, 2018. 24(2): p. 144-153.
54. Wennhold, K., A. Shimabukuro-Vornhagen, and M. von Bergwelt-Baildon, B Cell-Based Cancer Immunotherapy. Transfus Med Hemother, 2019. 46(1): p. 36-46.
55. Das, R., et al., Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J Clin Invest, 2018.128(2): p. 715-720.
Published
2021-01-20
How to Cite
TRIGO, Ana Catrina et al.
FLOW CYTOMETRIC EVALUATION OF PERIPHERAL BLOOD BIOMARKERS FOR SOLID TUMOURS IMMUNOTHERAPY GUIDING: A REVIEW.
Revista Portuguesa de Cirurgia, [S.l.], n. 49, p. 48-60, jan. 2021.
ISSN 2183-1165.
Available at: <https://revista.spcir.com/index.php/spcir/article/view/852>. Date accessed: 01 may 2024.
doi: https://doi.org/10.34635/rpc.852.
Section
Review Article
