Letter | Published:

Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma

Nature Medicinevolume 24pages15451549 (2018) | Download Citation

Abstract

Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has been very successful in treatment of melanoma. However, only a subset of patients with advanced tumors currently benefit from ICB therapies, which at times incur considerable side effects and costs. Constructing predictors of patient response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both ‘omics’ and response data. Here we build immuno-predictive score (IMPRES), a predictor of ICB response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (i) immune mechanisms underlying spontaneous regression in neuroblastoma can predict melanoma response to ICB, and (ii) key immune interactions can be captured via specific pairwise relations of the expression of immune checkpoint genes. IMPRES is validated on nine published datasets1,2,3,4,5,6 and on a newly generated dataset with 31 patients treated with anti-PD-1 and 10 with anti-CTLA-4, spanning 297 samples in total. It achieves an overall accuracy of AUC = 0.83, outperforming existing predictors and capturing almost all true responders while misclassifying less than half of the nonresponders. Future studies are warranted to determine the value of the approach presented here in other cancer types.

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Acknowledgements

The authors thank M. Leiserson, S. Patkar, E. Persi, W. Robinson, Z. Ronai, Y. Saadon, Y. Samuels and J. Wherry for their helpful comments. N.A., J.S.L. and E.R. were partially supported by a grant from the Israeli Science Foundation (ISF, grant no. 41/11) and R33-CA225291-01. E.R. is supported by the intramural program at the CCR, NCI. N.A. is supported by the NCI–UMD partnership for integrative cancer research. M.H. and K.F. are supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation. M.H. is supported by NIH grants 5P01CA114046, 5P50CA174523 and 1U54CA224070, and the Peer Reviewed Cancer Research Program Grant WX1XWH-16-1-0119 [CA150619].

Author information

Affiliations

  1. Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA

    • Noam Auslander
    • , Joo Sang Lee
    • , Sanna Madan
    •  & Eytan Ruppin
  2. Cancer Data Science Lab (CDSL), National Cancer Institute, National Institutes of Health, Rockville, MD, USA

    • Noam Auslander
    • , Joo Sang Lee
    • , Sanna Madan
    •  & Eytan Ruppin
  3. Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA

    • Gao Zhang
    •  & Meenhard Herlyn
  4. Massachusetts General Hospital Cancer Center, Boston, MA, USA

    • Dennie T. Frederick
    • , Benchun Miao
    • , Ryan J. Sullivan
    •  & Keith Flaherty
  5. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    • Tabea Moll
    •  & Genevieve Boland
  6. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA

    • Tian Tian
    •  & Zhi Wei

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Contributions

E.R. supervised the research. N.A. and E.R. conceived and designed the computational approach. N.A. performed statistical and machine-learning analysis. N.A. and J.S.L. analyzed the data. S.M. applied the CIBERSORT deconvolution tool. E.R., N.A. and G.Z. wrote the paper. M.H., K.F., G.B and J.S.L. helped write the paper. G.Z., B.M., D.T.F., T.M., T.T., Z.W. and R.J.S. collected the clinical and transcriptomic data under the supervision of G.Z., K.F., G.B. and M.H.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Eytan Ruppin.

Electronic supplementary material

  1. Supplementary Text and Figures

    Supplementary Figures 1–10 and Supplementary Tables 1, 2, 5, 6, 8 and 11

  2. Reporting Summary

  3. Supplementary Table 3

    Immune pathway enrichment P values and their correlation with IMPRES ratios

  4. Supplementary Table 4

    CIBERSORT analysis

  5. Supplementary Table 7

    Individual ICB response predictive power (AUC) of each of the IMPRES features

  6. Supplementary Table 9

    Melanoma patients’ identifiers and their ICB response annotations

  7. Supplementary Table 11

    IMPRES sensitivity analysis

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DOI

https://doi.org/10.1038/s41591-018-0157-9

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