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

Matters Arising to this article was published on 05 December 2019

A Publisher Correction to this article was published on 17 October 2018

This article has been updated


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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Fig. 1: Shared immune components between melanoma and neuroblastoma.
Fig. 2: Response and survival predictive performance of IMPRES.
Fig. 3: Comparative performance of IMPRES and its underlying features.

Change history


  1. 1.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  4. 4.

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  18. 18.

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  21. 21.

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

    Article  Google Scholar 

  22. 22.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  24. 24.

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  26. 26.

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

    CAS  Article  Google Scholar 

  27. 27.

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

    CAS  Article  Google Scholar 

  28. 28.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  30. 30.

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

    Article  Google Scholar 

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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




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.

Corresponding author

Correspondence to Eytan Ruppin.

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The authors declare no competing interests.

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Supplementary Text and Figures

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

Reporting Summary

Supplementary Table 3

Immune pathway enrichment P values and their correlation with IMPRES ratios

Supplementary Table 4

CIBERSORT analysis

Supplementary Table 7

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

Supplementary Table 9

Melanoma patients’ identifiers and their ICB response annotations

Supplementary Table 11

IMPRES sensitivity analysis

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Auslander, N., Zhang, G., Lee, J.S. et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med 24, 1545–1549 (2018).

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