Letter | Published:

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

Nature Medicinevolume 24pages15451549 (2018) | Download Citation


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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  1. 1.

    Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

  2. 2.

    Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

  3. 3.

    Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

  4. 4.

    Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

  5. 5.

    Prat, A. et al. Immune-related gene expression profiling after PD-1 blockade in non–small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma. Cancer Res. 77, 3540–3550 (2017).

  6. 6.

    Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e15 (2017).

  7. 7.

    Bramhall, R. J., Mahady, K. & Peach, A. H. S. Spontaneous regression of metastatic melanoma—clinical evidence of the abscopal effect. Eur. J. Surg. Oncol. 40, 34–41 (2014).

  8. 8.

    Kalialis, L. V., Drzewiecki, K. T. & Klyver, H. Spontaneous regression of metastases from melanoma: review of the literature. Melanoma Res. 19, 275–282 (2009).

  9. 9.

    Diede, S. J. Spontaneous regression of metastatic cancer: learning from neuroblastoma. Nat. Rev. Cancer 14, 71–72 (2014).

  10. 10.

    Brodeur, G. M. & Bagatell, R. Mechanisms of neuroblastoma regression. Nat. Rev. Clin. Oncol. 11, 704–713 (2014).

  11. 11.

    Cheung, N. K. V. et al. Ganglioside GD2 specific monoclonal antibody 3F8: a phase I study in patients with neuroblastoma and malignant melanoma. J. Clin. Oncol. 5, 1430–1440 (1987).

  12. 12.

    Yu, A. L. et al. Anti-GD2 antibody with GM-CSF, interleukin-2, and isotretinoin for neuroblastoma. N. Engl. J. Med. 363, 1324–1334 (2010).

  13. 13.

    Su, Z. et al. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol. 15, 523 (2014).

  14. 14.

    Jönsson, G. et al. Gene expression profiling–based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin. Cancer Res. 16, 3356–3367 (2010).

  15. 15.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

  16. 16.

    Simon, R., Radmacher, M. D., Dobbin, K. & McShane, L. M. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl Cancer Inst. 95, 14–18 (2003).

  17. 17.

    Tinker, A. V., Boussioutas, A. & Bowtell, D. D. L. The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9, 333–339 (2006).

  18. 18.

    Ransohoff, D. F. Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer 5, 142–149 (2005).

  19. 19.

    Zippelius, A., Schreiner, J., Herzig, P. & Müller, P. Induced PD-L1 expression mediates acquired resistance to agonistic anti-CD40 treatment. Cancer Immunol. Res. 3, 236–244 (2015).

  20. 20.

    Ahrends, T. et al. CD27 agonism plus PD-1 blockade recapitulates CD4+ T-cell help in therapeutic anticancer vaccination. Cancer Res. 76, 2921–2931 (2016).

  21. 21.

    Chen, L. & Flies, D. B. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat. Rev. Immunol. 13, 227–242 (2013).

  22. 22.

    Zhang, Q. & Vignali, D. A. A. Co-stimulatory and co-inhibitory pathways in autoimmunity. Immunity 44, 1034–1051 (2016).

  23. 23.

    Fuertes Marraco, S. A., Neubert, N. J., Verdeil, G. & Speiser, D. E. Inhibitory receptors beyond T cell exhaustion. Front. Immunol. 6, 310 (2015).

  24. 24.

    Ramsay, A. G. Immune checkpoint blockade immunotherapy to activate anti-tumour T-cell immunity. Br. J. Haematol. 162, 313–325 (2013).

  25. 25.

    Buchbinder, E. I. & Desai, A. CTLA-4 and PD-1 pathways: similarities, differences, and implications of their inhibition. Am. J. Clin. Oncol. 39, 98–106 (2016).

  26. 26.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  27. 27.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  28. 28.

    Jenkins, R. W. et al. Ex vivo profiling of PD-1 blockade using organotypic tumor spheroids. Cancer Discov. 8, 196–215 (2017).

  29. 29.

    Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

  30. 30.

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

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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


  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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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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