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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Change history
17 October 2018
In the version of this article originally published, there was an error in the URL linked to by an accession code in the data availability section of the methods. The erroneous URL was: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100351. The correct URL is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115821. The error has been corrected in the HTML and PDF versions of this article.
References
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
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).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
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).
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e15 (2017).
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).
Kalialis, L. V., Drzewiecki, K. T. & Klyver, H. Spontaneous regression of metastases from melanoma: review of the literature. Melanoma Res. 19, 275–282 (2009).
Diede, S. J. Spontaneous regression of metastatic cancer: learning from neuroblastoma. Nat. Rev. Cancer 14, 71–72 (2014).
Brodeur, G. M. & Bagatell, R. Mechanisms of neuroblastoma regression. Nat. Rev. Clin. Oncol. 11, 704–713 (2014).
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).
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).
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).
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).
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).
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).
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).
Ransohoff, D. F. Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer 5, 142–149 (2005).
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).
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).
Chen, L. & Flies, D. B. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat. Rev. Immunol. 13, 227–242 (2013).
Zhang, Q. & Vignali, D. A. A. Co-stimulatory and co-inhibitory pathways in autoimmunity. Immunity 44, 1034–1051 (2016).
Fuertes Marraco, S. A., Neubert, N. J., Verdeil, G. & Speiser, D. E. Inhibitory receptors beyond T cell exhaustion. Front. Immunol. 6, 310 (2015).
Ramsay, A. G. Immune checkpoint blockade immunotherapy to activate anti-tumour T-cell immunity. Br. J. Haematol. 162, 313–325 (2013).
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).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Jenkins, R. W. et al. Ex vivo profiling of PD-1 blockade using organotypic tumor spheroids. Cancer Discov. 8, 196–215 (2017).
Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
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
Authors and Affiliations
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Supplementary Text and Figures
Supplementary Figures 1–10 and Supplementary Tables 1, 2, 5, 6, 8 and 11
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
Rights and permissions
About this article
Cite this article
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). https://doi.org/10.1038/s41591-018-0157-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-018-0157-9
This article is cited by
-
T-cell infiltration and its regulatory mechanisms in cancers: insights at single-cell resolution
Journal of Experimental & Clinical Cancer Research (2024)
-
PD-1 defines a distinct, functional, tissue-adapted state in Vδ1+ T cells with implications for cancer immunotherapy
Nature Cancer (2024)
-
A method for predicting drugs that can boost the efficacy of immune checkpoint blockade
Nature Immunology (2024)
-
Synergistic induction of tertiary lymphoid structures by chemoimmunotherapy in bladder cancer
British Journal of Cancer (2024)
-
Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy
Nature Communications (2024)