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High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

An Author Correction to this article was published on 02 July 2018

This article has been updated

Abstract

Immune-checkpoint blockade has revolutionized cancer therapy. In particular, inhibition of programmed cell death protein 1 (PD-1) has been found to be effective for the treatment of metastatic melanoma and other cancers. Despite a dramatic increase in progression-free survival, a large proportion of patients do not show durable responses. Therefore, predictive biomarkers of a clinical response are urgently needed. Here we used high-dimensional single-cell mass cytometry and a bioinformatics pipeline for the in-depth characterization of the immune cell subsets in the peripheral blood of patients with stage IV melanoma before and after 12 weeks of anti-PD-1 immunotherapy. During therapy, we observed a clear response to immunotherapy in the T cell compartment. However, before commencing therapy, a strong predictor of progression-free and overall survival in response to anti-PD-1 immunotherapy was the frequency of CD14+CD16HLA-DRhi monocytes. We confirmed this by conventional flow cytometry in an independent, blinded validation cohort, and we propose that the frequency of monocytes in PBMCs may serve in clinical decision support.

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Figure 1: Stratification of responders and nonresponders, and identification of differences in immune cell populations using mass cytometry.
Figure 2: Differences in T cell activation status and in the frequency of the T cell subpopulations before and after 12 weeks of anti-PD-1 therapy in responders and nonresponders.
Figure 3: Increased activation in CD4+ T cells after the initiation of immunotherapy in responders.
Figure 4: Increased activation in CD8+ T cells after the initiation of immunotherapy in responders.
Figure 5: Patient stratification based on myeloid cell markers and expansion of classical monocytes in responders.
Figure 6: Enhanced activation of classical monocytes in responders and validation by conventional flow cytometry.

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  • 02 July 2018

    In the version of this article initially published, Figs. 5a,c and 6a were incorrect because of an error in a metadata spreadsheet that led to the healthy donor patient 2 (HD2) samples being used twice in the analysis of baseline samples and in the analysis at 12 weeks of anti-PD-1 therapy, while HD3 samples had not been used. Data from sets of both samples should have been used in the analyses. The influence of this error on population proportions, marker expression, P values and tSNE visualization (in Figs. 5a,c and 6a) was minimal (e.g., via clustering and variance calculations). Complete reanalysis of the dataset, now including data from both HD2 and HD3, resulted in minor changes to Figs. 5a,c and 6a as well as Supplementary Figs. 7, 9, 11 and 17. This error did not affect other analyses or any of the conclusions in the paper. Also, there was an error in the description of the n values in the original Fig. 6b legend. The legend originally read: "(R and NR, n = 4 for each group)". It should be: "(HD, n = 4; R and NR, n = 3 for each group)". In addition, the Fig. 6 legend originally read: "All patients in this study were analyzed (n = 51)". It should read: "All patients from the validation cohort were analyzed (n = 31), with any patient not making the 12-month endpoint (i.e., OS or follow-up) included in the 'No' column". Additionally, there were errors in the Supplementary Information. In the ‘Validation by flow cytometry’ section of the Supplementary Methods, the antibody list was missing some antibodies. It originally read: “CD11b-BrilliantViolett (BV) 421 (ICRF44), CD14-PE (HCD14), HLA-DR-FITC (L243), CD4-BV711 (OKT4), CD33-BV605 (WM53), and Live/dead-stain-NearInfraRed." This list should include CD19-BV605 (1D3) and CD3-BV785 (OKT3). In the "Correlation of PFS with monocyte frequency" section of the Supplementary Methods, there was an error in the sentence, "To compute the cumulative hazard function we used the previously calculated cutoff of 19.39% to create the 2 groups". The percentage was incorrect. It should be 19.38%. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. Topalian, S.L., Drake, C.G. & Pardoll, D.M. Targeting the PD-1/B7-H1(PD-L1) pathway to activate antitumor immunity. Curr. Opin. Immunol. 24, 207–212 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Topalian, S.L. et al. Safety, activity and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Brahmer, J. et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N. Engl. J. Med. 373, 123–135 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Motzer, R.J. et al. Nivolumab versus everolimus in advanced renal cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rizvi, N.A. et al. 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, 257–265 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ansell, S.M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma. N. Engl. J. Med. 372, 311–319 (2015).

    PubMed  Google Scholar 

  8. Center for Drug Evaluation Research. Approved Drugs—Hematology/Oncology (Cancer) Approvals & Safety Notifications (US Food and Drug Administration, 2016).

  9. Hamid, O. et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134–144 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nishino, M., Ramaiya, N.H., Hatabu, H. & Hodi, F.S. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14, 655–668 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wistuba-Hamprecht, K. et al. Establishing high-dimensional immune signatures from peripheral blood via mass cytometry in a discovery cohort of stage IV melanoma patients. J. Immunol. 198, 927–936 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Pérez-Callejo, D., Romero, A., Provencio, M. & Torrente, M. Liquid-biopsy-based biomarkers in non-small-cell lung cancer for diagnosis and treatment monitoring. Transl. Lung Cancer Res. 5, 455–465 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Levine, J.H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

    Article  PubMed  Google Scholar 

  15. Weber, L.M. & Robinson, M. D. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89, 1084–1096 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Maaten, L.V.D. & Hinton, G. Visualizing data using tSNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  17. Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. 6, 748 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Huang, A.C. et al. T cell invigoration to tumor burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kamphorst, A.O. et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl. Acad. Sci. USA 114, 4993–4998 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Arvaniti, E. & Claassen, M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun. 8, 14825 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Brahmer, J.R. et al. Phase 1 study of single-agent anti-programmed-death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics and immunologic correlates. J. Clin. Oncol. 28, 3167–3175 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brahmer, J.R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455–2465 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Weber, J.S. et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomized, controlled, open-label, phase 3 trial. Lancet Oncol. 16, 375–384 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372, 2521–2532 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Ribas, A. et al. Association of pembrolizumab with tumor response and survival among patients with advanced melanoma. J. Am. Med. Assoc. 315, 1600–1609 (2016).

    Article  CAS  Google Scholar 

  26. Ostrand-Rosenberg, S. & Sinha, P. Myeloid-derived suppressor cells: linking inflammation and cancer. J. Immunol. 182, 4499–4506 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Gebhardt, C. et al. Myeloid cells and related chronic inflammatory factors as novel predictive markers in melanoma treatment with ipilimumab. Clin. Cancer Res. 21, 5453–5459 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Meyer, C. et al. Frequencies of circulating MDSC correlate with clinical outcome of melanoma patients treated with ipilimumab. Cancer Immunol. Immunother. 63, 247–257 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. 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. 22, 5661–5672 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Komohara, Y., Jinushi, M. & Takeya, M. Clinical significance of macrophage heterogeneity in human malignant tumors. Cancer Sci. 105, 1–8 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Romano, E. et al. Ipilimumab-dependent cell-mediated cytotoxicity of regulatory T cells ex vivo by nonclassical monocytes in melanoma patients. Proc. Natl. Acad. Sci. USA 112, 6140–6145 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chavan, R. et al. Untreated stage IV melanoma patients exhibit abnormal monocyte phenotypes and decreased functional capacity. Cancer Immunol. Res. 2, 241–248 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Zaretsky, J.M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bellucci, R. et al. Interferon-γ-induced activation of JAK1 and JAK2 suppresses tumor cell susceptibility to NK cells through upregulation of PD-L1 expression. OncoImmunology 4, e1008824 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Daud, A.I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Herbst, R.S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kluger, H.M. et al. Characterization of PD-L1 expression and associated T cell infiltrates in metastatic melanoma samples from variable anatomic sites. Clin. Cancer Res. 21, 3052–3060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. von Andrian, U.H. & Mempel, T.R. Homing and cellular traffic in lymph nodes. Nat. Rev. Immunol. 3, 867–878 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Takizawa, H., Regoes, R.R., Boddupalli, C.S., Bonhoeffer, S. & Manz, M.G. Dynamic variation in cycling of hematopoietic stem cells in steady state and inflammation. J. Exp. Med. 208, 273–284 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nagai, Y. et al. Toll-like receptors on hematopoietic progenitor cells stimulate innate immune system replenishment. Immunity 24, 801–812 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Caux, C., Moreau, I., Saeland, S. & Banchereau, J. Interferon-γ enhances factor-dependent myeloid proliferation of human CD34+ hematopoietic progenitor cells. Blood 79, 2628–2635 (1992).

    Article  CAS  PubMed  Google Scholar 

  43. Hartmann, F.J. et al. High-dimensional single-cell analysis reveals the immune signature of narcolepsy. J. Exp. Med. 213, 2621–2633 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Spidlen, J. & Brinkman, R.R. Use FlowRepository to share your clinical data upon study publication. Cytometry B Clin. Cytom. (2016).

  45. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Patro, R., Duggal, G., Love, M.I., Irizarry, R.A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Soneson, C., Love, M.I. & Robinson, M.D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. McCarthy, D.J., Chen, Y. & Smyth, G.K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank V. Tosevski and T.M. Brodie (mass cytometry core facility, University of Zurich), A. Langer (Department of Dermatology, University of Zurich), and C. Beisel and K. Eschbach (Genomics Facility, ETH Basel) for excellent technical assistance and N. Nunes, B. Chatterjee, E. Terskikh, and C. Gujer (all from the Institute of Experimental Immunology, University Zurich), A. Zollinger (Swiss Institute of Bioinformatics, Lausanne), all members of the COST Action BM1404 Mye-EUNITER (http://www.mye-euniter.eu), and P. Cheng (University of Zurich) for discussions. We also thank C. Guglietta for graphical design and layout. This work received funding from the University Research Priority Program (URPP) in Translational Cancer Research (C.K.), the Swiss National Science Foundation (grants 310030_146130 and 316030_150768; B.B.), the European Union FP7 project ATECT (B.B.), and the European Training Network MELGEN (M.P.L.).

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C.K., M.P.L., and B.B. conceived the study and analyzed data; C.K., S.G., and B.B. designed and performed the experiments; F.J.H. and S.G. assisted with the experiments; S.S., R.D., and M.P.L. provided clinical samples and performed statistical analyses of clinical parameters; R.D. and M.P.L. analyzed histology; M.N., L.M.W., and M.D.R. provided analysis algorithms and analyzed data; C.K. and S.G. wrote the manuscript; M.P.L., M.D.R., and B.B. edited the manuscript; and all authors read and gave final approval to submit the manuscript.

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Correspondence to Carsten Krieg, Mitchell P Levesque or Burkhard Becher.

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The authors declare no competing financial interests.

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Krieg, C., Nowicka, M., Guglietta, S. et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24, 144–153 (2018). https://doi.org/10.1038/nm.4466

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