Advertisement

Peripheral changes in immune cell populations and soluble mediators after anti-PD-1 therapy in non-small cell lung cancer and renal cell carcinoma patients

  • Estefanía Paula Juliá
  • Pablo Mandó
  • Manglio Miguel Rizzo
  • Gerardo Rubén Cueto
  • Florencia Tsou
  • Romina Luca
  • Carmen Pupareli
  • Alicia Inés Bravo
  • Walter Astorino
  • José Mordoh
  • Claudio Martín
  • Estrella Mariel LevyEmail author
Original Article

Abstract

Patients with non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC) have shown benefit from anti-PD-1 therapies. However, not all patients experience tumor shrinkage, durable responses or prolonged survival, demonstrating the need to find response markers. In blood samples from NSCLC and RCC patients obtained before and after anti-PD-1 treatment, we studied leukocytes by complete blood cell count, lymphocyte subsets using flow cytometry and plasma concentration of nine soluble mediators, in order to find predictive biomarkers of response and to study changes produced after anti-PD-1 therapy. In baseline samples, discriminant analysis revealed a combination of four variables that helped differentiate stable disease-response (SD-R) from progressive disease (PD) patients: augmented frequency of central memory CD4+ T cells and leukocyte count was associated with response while increased percentage of PD-L1+ natural killer cells and naïve CD4+ T cells was associated with lack of response. After therapy, differential changes between responders and non-responders were found in leukocytes, T cells and TIM-3+ T cells. Patients with progressive disease showed an increase in the frequency of TIM-3 expressing CD4+ and CD8+ T cells, whereas SD-R patients showed a decrease in these subsets. Our findings indicate that a combination of immune variables from peripheral blood (PB) could be useful to distinguish response groups in NSCLC and RCC patients treated with anti-PD-1 therapy. Frequency of TIM-3+ T cells showed differential changes after treatment in PD vs SD-R patients, suggesting that it may be an interesting marker for monitoring progression during therapy.

Keywords

Anti-PD-1 therapy Nivolumab Pembrolizumab NSCLC Renal cell carcinoma TIM-3 

Abbreviations

ALC

Absolute leukocyte count

CM

Central memory

CBC

Complete blood cell count

CRP

C-reactive protein

EM

Effector memory

FSC-A

Forward scatter-area

FSC-H

Forward scatter-height

NLR

Neutrophil-to-lymphocyte ratio

PD

Progressive disease

RCC

Renal cell carcinoma

SSC-A

Side scatter-area

SD-R

Stable disease-response

TIM-3

T cell immunoglobulin and mucin-domain containing-3

TE

Terminal effector

Notes

Acknowledgements

We thank Holliday Cartar for her assistance in language correction. We thank Dr. Laura Noro and all the laboratory staff from Alexander Fleming Institute for their help in this study.

Author contributions

EPJ collected, analyzed and interpreted the data, and wrote the manuscript. PM and EML analyzed and interpreted the data, and wrote the manuscript. MMR, FT and RL contributed with patients’ clinical data analysis. GRC contributed to the statistical analysis and performed discriminant analyses. AIB and WA performed IHC analysis. JM, CP and CM interpreted the data and revised the manuscript. All authors contributed to manuscript revision; read and approved the final manuscript version.

Funding

This work was supported by grants from Fundación Sales, Fundación Cáncer, Fundación Pedro F. Mosoteguy, Argentina. José Mordoh and Estrella Mariel Levy are members of Consejo Nacional de Investigaciones Científicas y Técnicas-CONICET. Estefanía Paula Juliá is a fellow from Consejo Nacional de Investigaciones Científicas y Técnicas-CONICET.

Compliance with ethical standards

Conflict of interest

Claudio Martín has served as speaker and advisory board member for Bristol Myers Squibb and Merck Sharp and Dohme. Carmen Pupareli has served as speaker and advisor board member for Merck Sharp and Dohme and as speaker for Bristol Myers Squibb. The authors declare that there is no other conflict of interest.

Ethical approval and ethical standards

All procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Comité de Ética en Investigación del Instituto Alexander Fleming CEIAF, approval number: 616, 14th June 2016.

Informed consent

All samples were taken after patients gave written informed consent approved by Comité de Ética en Investigación del Instituto Alexander Fleming CEIAF. Patients consented to the use of their specimens and data for research and for publication.

Supplementary material

262_2019_2391_MOESM1_ESM.pdf (810 kb)
Supplementary material 1 (PDF 811 kb)

References

  1. 1.
    Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WEE, Poddubskaya E et al (2015) Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 373:123–135CrossRefGoogle Scholar
  2. 2.
    Ferris RL, Blumenschein G, Fayette J, Guigay J, Colevas AD, Licitra L et al (2016) Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 375:1856–1867CrossRefGoogle Scholar
  3. 3.
    Hodi FS, Chesney J, Pavlick AC, Robert C, Grossmann KF, McDermott DF et al (2016) Combined nivolumab and ipilimumab versus ipilimumab alone in patients with advanced melanoma: 2-year overall survival outcomes in a multicentre, randomised, controlled, phase 2 trial. Lancet Oncol 17:1558–1568CrossRefGoogle Scholar
  4. 4.
    Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S et al (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 373:1803–1813CrossRefGoogle Scholar
  5. 5.
    Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L et al (2015) Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 372:320–330CrossRefGoogle Scholar
  6. 6.
    Hamid O, Robert C, Daud A, Hodi FS, Hwu W-J, Kefford R et al (2013) Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med 369:134–144CrossRefGoogle Scholar
  7. 7.
    Weber JS, Kudchadkar RR, Yu B, Gallenstein D, Horak CE, Inzunza HD et al (2013) Safety, efficacy, and biomarkers of nivolumab with vaccine in ipilimumab-refractory or -Naive Melanoma. J Clin Oncol 31:4311–4318CrossRefGoogle Scholar
  8. 8.
    Weber JS, D’Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B et al (2015) Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 16:375–384CrossRefGoogle Scholar
  9. 9.
    Ribas A, Puzanov I, Dummer R, Schadendorf D, Hamid O, Robert C et al (2015) Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): a randomised, controlled, phase 2 trial. Lancet Oncol 16:908–918CrossRefGoogle Scholar
  10. 10.
    Brahmer JR, Tykodi SS, Chow LQM, Hwu W-J, Topalian SL, Hwu P et al (2012) Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med 366:2455–2465CrossRefGoogle Scholar
  11. 11.
    Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR et al (2015) Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol 33:1430–1437CrossRefGoogle Scholar
  12. 12.
    McDermott DF, Sosman JA, Sznol M, Massard C, Gordon MS, Hamid O et al (2016) Atezolizumab, an anti-programmed death-ligand 1 antibody, in metastatic renal cell carcinoma: long-term safety, clinical activity, and immune correlates from a phase ia study. J Clin Oncol 34:833–842CrossRefGoogle Scholar
  13. 13.
    Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS et al (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515:563–567CrossRefGoogle Scholar
  14. 14.
    Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP et al (2015) Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med 372:2018–2028CrossRefGoogle Scholar
  15. 15.
    Rizvi NA, Mazières J, Planchard D, Stinchcombe TE, Dy GK, Antonia SJ et al (2015) Activity and safety of nivolumab, an anti-PD-1 immune checkpoint inhibitor, for patients with advanced, refractory squamous non-small-cell lung cancer (CheckMate 063): a phase 2, single-arm trial. Lancet Oncol. 16:257–265CrossRefGoogle Scholar
  16. 16.
    Herbst RS, Baas P, Kim D-W, Felip E, Pérez-Gracia JL, Han J-Y et al (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387:1540–1550CrossRefGoogle Scholar
  17. 17.
    Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J et al (2016) Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 387:1837–1846CrossRefGoogle Scholar
  18. 18.
    Gibney GT, Weiner LM, Atkins MB (2016) Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 17:e542–e551CrossRefGoogle Scholar
  19. 19.
    Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH et al (2014) Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 20:5064–5074CrossRefGoogle Scholar
  20. 20.
    Topalian SL, Taube JM, Anders RA, Pardoll DM (2016) Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 16:275–287CrossRefGoogle Scholar
  21. 21.
    Di Rienzo JA, Casanoves F, Balzarini MG, Gonzalez L, Tablada MRC (2017) InfoStat versión 2017. Grupo InfoStat, FCA, Universidad Nacional de Córdoba, ArgentinaGoogle Scholar
  22. 22.
    Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New YorkCrossRefGoogle Scholar
  23. 23.
    Rohart F, Gautier B, Singh A, Lê Cao KA (2017) mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13:e1005752CrossRefGoogle Scholar
  24. 24.
    Trivittayasil V, Tsuta M, Kasai S, Matsuo Y, Sekiyama Y, Shoji T et al (2018) Classification of 1-methylcyclopropene treated apples by fluorescence fingerprint using partial least squares discriminant analysis with stepwise selectivity ratio variable selection method. Chemom Intell Lab Syst. 175:30–36CrossRefGoogle Scholar
  25. 25.
    Wei T, Simko V (2017) R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). Available from https://github.com/taiyun/corrplot. Accessed Jan 2019
  26. 26.
    Callea M, Albiges L, Gupta M, Cheng S-C, Genega EM, Fay AP et al (2015) Differential expression of PD-L1 between primary and metastatic sites in clear-cell renal cell carcinoma. Cancer Immunol Res 3:1158–1164CrossRefGoogle Scholar
  27. 27.
    Jilaveanu LB, Shuch B, Zito CR, Parisi F, Barr M, Kluger Y et al (2014) PD-L1 expression in clear cell renal cell carcinoma: an analysis of nephrectomy and sites of metastases. J Cancer. 5:166–172CrossRefGoogle Scholar
  28. 28.
    Munari E, Zamboni G, Marconi M, Sommaggio M, Brunelli M, Martignoni G et al (2017) PD-L1 expression heterogeneity in non-small cell lung cancer: evaluation of small biopsies reliability. Oncotarget. 8:90123–90131CrossRefGoogle Scholar
  29. 29.
    Casadevall D, Clavé S, Taus Á, Hardy-Werbin M, Rocha P, Lorenzo M et al (2017) Heterogeneity of tumor and immune cell PD-L1 expression and lymphocyte counts in surgical NSCLC samples. Clin Lung Cancer 18(682–691):e5Google Scholar
  30. 30.
    Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM et al (2018) High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24:144–153CrossRefGoogle Scholar
  31. 31.
    Manjarrez-Orduño N, Menard LC, Kansal S, Fischer P, Kakrecha B, Jiang C et al (2018) Circulating T cell subpopulations correlate with immune responses at the tumor site and clinical response to PD1 inhibition in non-small cell lung cancer. Front Immunol. 9:1–9CrossRefGoogle Scholar
  32. 32.
    Wei SC, Duffy CR, Allison JP (2018) Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov 8:1069–1086CrossRefGoogle Scholar
  33. 33.
    O’Donnell JS, Long GV, Scolyer RA, Teng MWL, Smyth MJ (2017) Resistance to PD1/PDL1 checkpoint inhibition. Cancer Treat Rev 52:71–81CrossRefGoogle Scholar
  34. 34.
    Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A (2017) Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168:707–723CrossRefGoogle Scholar
  35. 35.
    Ameratunga M, Chénard-Poirier M, Moreno Candilejo I, Pedregal M, Lui A, Dolling D et al (2018) Neutrophil-lymphocyte ratio kinetics in patients with advanced solid tumours on phase I trials of PD-1/PD-L1 inhibitors. Eur J Cancer 89:56–63CrossRefGoogle Scholar
  36. 36.
    Sacdalan DB, Lucero JA, Sacdalan D (2018) Prognostic utility of baseline neutrophil-to-lymphocyte ratio in patients receiving immune checkpoint inhibitors: a review and meta-analysis. Onco Targets Ther. 11:955–965CrossRefGoogle Scholar
  37. 37.
    Weide B, Martens A, Hassel JC, Berking C, Postow MA, Bisschop K et al (2016) Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin Cancer Res 22:5487–5496CrossRefGoogle Scholar
  38. 38.
    Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M, Zhou J et al (2018) Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer. 6:18CrossRefGoogle Scholar
  39. 39.
    Sanmamed MF, Perez-Gracia JL, Schalper KA, Fusco JP, Gonzalez A, Rodriguez-Ruiz ME et al (2017) Changes in serum interleukin-8 (IL-8) levels reflect and predict response to anti-PD-1 treatment in melanoma and non-small-cell lung cancer patients. Ann Oncol 28:1988–1995CrossRefGoogle Scholar
  40. 40.
    Thommen DS, Schreiner J, Muller P, Herzig P, Roller A, Belousov A et al (2015) Progression of lung cancer is associated with increased dysfunction of T cells defined by coexpression of multiple inhibitory receptors. Cancer Immunol Res 3:1344–1355CrossRefGoogle Scholar
  41. 41.
    Koyama S, Akbay EA, Li YY, Herter-Sprie GS, Buczkowski KA, Richards WG et al (2016) Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun. 7:10501CrossRefGoogle Scholar
  42. 42.
    Anderson AC, Joller N, Kuchroo VK (2016) Lag-3, Tim-3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity 44:989–1004CrossRefGoogle Scholar
  43. 43.
    Kato R, Yamasaki M, Urakawa S, Nishida K, Makino T, Morimoto-Okazawa A et al (2018) Increased Tim-3 + T cells in PBMCs during nivolumab therapy correlate with responses and prognosis of advanced esophageal squamous cell carcinoma patients. Cancer Immunol Immunother 67:1673–1683CrossRefGoogle Scholar
  44. 44.
    Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC (2010) Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J Exp Med 207:2187–2194CrossRefGoogle Scholar
  45. 45.
    Ngiow SF, von Scheidt B, Akiba H, Yagita H, Teng MWL, Smyth MJ (2011) Anti-TIM3 antibody promotes T cell IFN-—mediated antitumor immunity and suppresses established tumors. Cancer Res 71:3540–3551CrossRefGoogle Scholar
  46. 46.
    Zhou Q, Munger ME, Veenstra RG, Weigel BJ, Hirashima M, Munn DH et al (2011) Coexpression of Tim-3 and PD-1 identifies a CD8 + T-cell exhaustion phenotype in mice with disseminated acute myelogenous leukemia. Blood 117:4501–4510CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Estefanía Paula Juliá
    • 1
  • Pablo Mandó
    • 1
    • 2
  • Manglio Miguel Rizzo
    • 2
  • Gerardo Rubén Cueto
    • 3
  • Florencia Tsou
    • 4
  • Romina Luca
    • 2
  • Carmen Pupareli
    • 4
  • Alicia Inés Bravo
    • 5
  • Walter Astorino
    • 4
  • José Mordoh
    • 1
    • 4
    • 6
  • Claudio Martín
    • 4
  • Estrella Mariel Levy
    • 1
    Email author
  1. 1.Centro de Investigaciones OncológicasFundación Cáncer-FUCACiudad Autónoma de Buenos AiresArgentina
  2. 2.Fundación Cáncer-FUCACiudad Autónoma de Buenos AiresArgentina
  3. 3.Grupo de Bioestadística Aplicada, Departamento de Ecología, Genética y Evolución, Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA-UBA/CONICET), Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresCiudad Autónoma de Buenos AiresArgentina
  4. 4.Instituto Alexander FlemingCiudad Autónoma de Buenos AiresArgentina
  5. 5.Hospital Interzonal Eva Perón HIGA, San MartínBuenos AiresArgentina
  6. 6.Fundación Instituto Leloir-IIBBACiudad Autónoma de Buenos AiresArgentina

Personalised recommendations