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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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  • 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.

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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.).

Author information

Author notes

    • Carsten Krieg

    Present address: Department of Microbiology and Immunology and Department of Dermatology, Hollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina, USA.

    • Mitchell P Levesque
    •  & Burkhard Becher

    These authors jointly directed this work.

Affiliations

  1. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland.

    • Carsten Krieg
    • , Felix J Hartmann
    •  & Burkhard Becher
  2. Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

    • Malgorzata Nowicka
    • , Lukas M Weber
    •  & Mark D Robinson
  3. Swiss Institute of Bioinformatics (SIB), University of Zurich, Zurich, Switzerland.

    • Malgorzata Nowicka
    • , Lukas M Weber
    •  & Mark D Robinson
  4. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.

    • Silvia Guglietta
  5. Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.

    • Sabrina Schindler
    • , Reinhard Dummer
    •  & Mitchell P Levesque

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Contributions

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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

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

Supplementary information

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  1. 1.

    Supplementary Figures & Tables

    Supplementary Methods, Supplementary Tables 1–3 & Supplementary Figures 1–17

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    Life Sciences Reporting Summary

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DOI

https://doi.org/10.1038/nm.4466