AVALIAÇÃO DE BIOMARCADORES NO SANGUE PERIFÉRICO POR CITOMETRIA DE FLUXO COMO MEIO DE ORIENTAÇÃO PARA A IMUNOTERAPIA EM TUMORES SÓLIDOS: REVISÃO DA LITERATURA

  • Ana Catrina Trigo Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Porto, Portugal; Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto, Portugal http://orcid.org/0000-0002-0383-7266
  • Patrícia Maia Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal; Department of Chemistry, University of Aveiro, Aveiro, Portugal; Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto, Portugal http://orcid.org/0000-0003-1216-3280
  • Inês Godinho Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal http://orcid.org/0000-0001-7952-8702
  • Catarina A. Rodrigues Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, 4200-162 Porto, Portugal; Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal http://orcid.org/0000-0002-1305-8055
  • Maria Emília Sousa Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal http://orcid.org/0000-0002-4965-9530
  • Ana Marta Pires Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal http://orcid.org/0000-0002-9011-5818
  • Carla Azevedo Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal http://orcid.org/0000-0002-6360-2100
  • Lúcio Lara Santos Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto, Portugal; Health Science Faculty, University of Fernando Pessoa, Porto, Portugal; Department of Surgical Oncology, Portuguese Institute of Oncology of Porto, Porto, Portugal; Porto Comprehensive Cancer Centre (P.ccc), Porto, Portugal http://orcid.org/0000-0002-0521-5655
  • Carlos Palmeira Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal; Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto, Portugal; Health Science Faculty, University of Fernando Pessoa, Porto, Portugal; Porto Comprehensive Cancer Centre (P.ccc), Porto, Portugal http://orcid.org/0000-0002-4833-2202
  • Gabriela Martins Immunology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal; Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto, Portugal; Porto Comprehensive Cancer Centre (P.ccc), Porto, Portugal http://orcid.org/0000-0002-1367-8852

Resumo

O tratamento do cancro é uma área em intensa e permanente atualização, nomeadamente com a descoberta da imunoterapia como modalidade terapêutica clinicamente eficaz para diversos tipos de cancro. Entre estas terapias inovadoras, as terapias com inibidores de checkpoints imunológicos (ICI) têm demonstrado apresentar respostas significativas e duradouras. No entanto, esta resposta não ocorre para todos os doentes, ou seja, alguns pacientes não beneficiam desta terapia. Devido a esta heterogeneidade na resposta à imunoterapia, persiste a necessidade urgente de identificar e estabelecer biomarcadores que permitam a identificação dos doentes que irão responder à terapia, poupando os que não respondem aos efeitos adversos. Para além deste aspeto, parece promissor o impacto na clínica da utilização destes biomarcadores na monitorização da resposta durante o tratamento. Dado o reconhecido papel do sistema imunológico na resposta anti-tumoral, estas células têm sido intensamente estudadas como potenciais biomarcadores. Com este objetivo, o sangue periférico (SP), tem revelado grande interesse e importância, dada a sua fácil acessibilidade e natureza menos invasiva. A avaliação detalhada e integral da imunidade periférica, exige uma metodologia multiparamétrica como a citometria de fluxo, recorrendo à utilização de marcadores de linhagem simultaneamente com marcadores de maturação, ativação e estados funcionais. Com esta revisão bibliográfica narrativa, pretendeu-se descrever o “estado da arte” sobre o estudo por citometria de fluxo das populações celulares do sistema imunológico no SP, como potenciais biomarcadores para a terapia com ICI em tumores sólidos. Os resultados encontrados são apresentados para cada uma das populações principais e suas subpopulações, nomeadamente linfócitos T, células supressoras derivadas da linhagem mieloide (MDSCs), neutrófilos, eosinófilos, células dendríticas (DC), células natural killer (NK), monócitos, e linfócitos B. 

Downloads

Dados de Download não estão ainda disponíveis.

Referências

1. Burugu, S., A.R. Dancsok, and T.O. Nielsen, Emerging targets in cancer immunotherapy. Semin Cancer Biol, 2018. 52(Pt 2): p. 39-52.

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.
Publicado
2021-01-20
Como Citar
TRIGO, Ana Catrina et al. AVALIAÇÃO DE BIOMARCADORES NO SANGUE PERIFÉRICO POR CITOMETRIA DE FLUXO COMO MEIO DE ORIENTAÇÃO PARA A IMUNOTERAPIA EM TUMORES SÓLIDOS: REVISÃO DA LITERATURA. Revista Portuguesa de Cirurgia, [S.l.], n. 49, p. 48-60, jan. 2021. ISSN 2183-1165. Disponível em: <https://revista.spcir.com/index.php/spcir/article/view/852>. Acesso em: 30 mar. 2023. doi: https://doi.org/10.34635/rpc.852.
Secção
Artigos de Revisão