Immunologic responses to anti-PD-1 therapy in melanoma patients occur rapidly with pharmacodynamic T cell responses detectable in blood by 3 weeks. It is unclear, however, whether these early blood-based observations translate to the tumor microenvironment. We conducted a study of neoadjuvant/adjuvant anti-PD-1 therapy in stage III/IV melanoma. We hypothesized that immune reinvigoration in the tumor would be detectable at 3 weeks and that this response would correlate with disease-free survival. We identified a rapid and potent anti-tumor response, with 8 of 27 patients experiencing a complete or major pathological response after a single dose of anti-PD-1, all of whom remain disease free. These rapid pathologic and clinical responses were associated with accumulation of exhausted CD8 T cells in the tumor at 3 weeks, with reinvigoration in the blood observed as early as 1 week. Transcriptional analysis demonstrated a pretreatment immune signature (neoadjuvant response signature) that was associated with clinical benefit. In contrast, patients with disease recurrence displayed mechanisms of resistance including immune suppression, mutational escape, and/or tumor evolution. Neoadjuvant anti-PD-1 treatment is effective in high-risk resectable stage III/IV melanoma. Pathological response and immunological analyses after a single neoadjuvant dose can be used to predict clinical outcome and to dissect underlying mechanisms in checkpoint blockade.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $18.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Custom code used to analyze tumor whole exome sequencing data is available at https://zenodo.org/badge/latestdoi/162582612
NanoString data that support the findings have been deposited in the NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE123728. DNA whole exome sequencing data have been deposited in SRA and are accessible under SRA accession number PRJNA510621. All other relevant data are available from the corresponding author upon reasonable request.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).
Vesely, M. D., Kershaw, M. H., Schreiber, R. D. & Smyth, M. J. Natural innate and adaptive immunity to cancer. Annu. Rev. Immunol. 29, 235–271 (2011).
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
Kamphorst, A. O. et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl Acad. Sci. USA 114, 4993–4998 (2017).
Hellmann, M. D. et al. Pathological response after neoadjuvant chemotherapy in resectable non-small-cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint. Lancet Oncol. 15, e42–e50 (2014).
Forde, P. M. et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N. Engl. J. Med. 378, 1976–1986 (2018).
Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).
Simoni, Y. et al. Bystander CD8(+) T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).
Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).
Blackburn, S. D. et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat. Immunol. 10, 29–37 (2009).
Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).
Zappasodi, R., Merghoub, T. & Wolchok, J. D. Emerging concepts for immune checkpoint blockade-based combination therapies. Cancer Cell. 33, 581–598 (2018).
Ayers, M. et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
Harlin, H. et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 69, 3077–3085 (2009).
Taube, J. M. et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra137 (2012).
Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).
Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).
Guo, G., Yu, M., Xiao, W., Celis, E. & Cui, Y. Local activation of p53 in the tumor microenvironment overcomes immune suppression and enhances antitumor immunity. Cancer Res. 77, 2292–2305 (2017).
Daud, A. I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).
Restifo, N. P. et al. Loss of functional beta 2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl Cancer Inst. 88, 100–108 (1996).
Sucker, A. et al. Genetic evolution of T-cell resistance in the course of melanoma progression. Clin. Cancer Res. 20, 6593–6604 (2014).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Engblom, C., Pfirschke, C. & Pittet, M. J. The role of myeloid cells in cancer therapies. Nat. Rev. Cancer 16, 447–462 (2016).
Ribas, A. Adaptive immune resistance: how cancer protects from immune attack. Cancer Discov. 5, 915–919 (2015).
Teng, M. W., Ngiow, S. F., Ribas, A. & Smyth, M. J. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 75, 2139–2145 (2015).
Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502.e15 (2017).
Mihm, M. C. Jr., Clemente, C. G. & Cascinelli, N. Tumor infiltrating lymphocytes in lymph node melanoma metastases: a histopathologic prognostic indicator and an expression of local immune response. Lab. Invest. 74, 43–47 (1996).
Heinze, G. & Schemper, M. A solution to the problem of monotone likelihood in Cox regression. Biometrics 57, 114–119 (2001).
Brahmer, J. R. et al. Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J. Clin. Oncol. 28, 3167–3175 (2010).
Chen, M. et al. Development and validation of a novel clinical fluorescence in situ hybridization assay to detect JAK2 and PD-L1 amplification: a fluorescence in situ hybridization assay for JAK2 and PD-L1 amplification. Mod. Pathol. 30, 1516–1526 (2017).
Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv:1303.3997v2 [q-bio.GN] (2013), https://arxiv.org/abs/1303.3997.
DePristo, M. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Van der Auwera, G. A. et al. From fastq data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
Karosiene, E., Lundegaard, C., Lund, O. & Nielsen, M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64, 177–186 (2012).
Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).
Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
Clinical and correlative studies were supported in part by the SPORE in Skin Cancer: P50-CA174523 (X.X., K.L.N., L.M.S., R.K.A., R.M., G.C.K.), P01-CA114046 (X.X.), T32-2T32CA009615 (A.C.H.); the NIH/NCI Cancer Center Support Grant P30-CA016520 (R.K.A., K.L.N., L.M.S., R.M.), and NIH grants AI105343, AI108545, AI117950, AI082630, CA210944 (E.J.W.), and AI114852 (R.S.H.); the Tara Miller Foundation (A.C.H.); the Melanoma Research Alliance (E.J.W.); the David and Hallee Adelman Immunotherapy Research Fund (E.J.W.); the Heisenberg program BE5496/2-1 of the DFG (B.B.); and the Parker Institute for Cancer Immunotherapy Bridge Scholar Award (A.C.H.). Merck, Inc. supplied drugs and supported clinical and translational aspects of this study. The Human Immunology Core and the Tumor Tissue and Biospecimen Bank of the University of Pennsylvania (supported by P30-CA016520) assisted in tissue collection, processing, and storage.
Percentage viable tumor between brisk (n = 9) versus non-brisk/absent tumors (n = 11). P value calculated using two-sided Mann–Whitney test.
a, Percentage Ki67 expression in CD8, conventional CD4, and Treg (FoxP3+ CD4) T cells pre and post in blood (n = 28 independent paired patient samples for CD8 comparisons, n = 17 independent paired patient samples for CD4 comparisons, and n = 27 independent patient samples for Treg comparisons). Two-sided Wilcoxon matched-pairs test was performed for CD8 and Treg comparisons. Two-sided t-test was performed for CD4 comparison. b, Percentage Ki67 expression in CD8, conventional CD4, and Treg (FoxP3+ CD4) T cells pre and post in tumor (n = 26 independent paired patient samples for CD8 comparisons, n = 15 independent paired patient samples for CD4 comparisons, and n = 25 independent paired patient samples for Treg comparisons). Two-sided Wilcoxon matched-pairs test was performed for CD4 and Treg comparisons. Two-sided t-test was performed for CD8 comparison.
a, Changes in tumor PD-L1 pre- versus post-treatment using immunohistochemistry staining (n = 9 independent paired patient samples). **P <0.01 using two-sided Wilcoxon matched-pairs test. b, Correlation of percentage of Ki67+ in non-naïve CD8 T cells versus percentage of Ki67+ in Tregs (FoxP3+CD4) (n = 21 independent patient samples); R score and P value generated using Pearson’s correlation. c, Thirty-three post-treatment immune parameters classified by recurrence using random forest analysis and ranked by importance score (n = 21 independent patient samples). Error bar denotes mean ± s.d. for 1,000 random forest iterations. d, Percentage expression of selected markers in tumor between patients with recurrence (9 independent patient samples) and no recurrence (12 independent patient samples). P value calculated using two-sided Mann–Whitney test. e, Correlation of percentage of Ki67+ in Tregs (FoxP3+ CD4) versus percentage of Eomes+ T-bet- in non-naïve CD8 (n = 21 independent patient samples); R score and P value generated using Pearson’s correlation. f, Twenty-five pretreatment immune parameters classified by recurrence using random forest analysis and ranked by importance score (n = 21 independent patient samples). Error bar denotes mean ± s.d. for 1,000 random forest iterations. g, Percentage expression of selected markers in tumor between patients with recurrence (9 independent patient samples) and no recurrence (12 independent patient samples). Two-sided t-test was used for CD45RA-CD27+ and CD45RA+CD27+ comparisons. Two-sided Mann–Whitney test was used for CD8 Ki67+ and CD4 Ki67+ comparisons. Error bar denotes mean ± s.d. h, Scatter plot of percentage of Ki67+ in non-naïve CD8 versus percentage of Ki67+in FoxP3+ CD4 (Tregs) at pretreatment stratified by recurrence status. Dotted line denotes non-naïve CD8 Ki67+ of 5.5 calculated by CART analysis as the optimal cut point separating recurrence versus no recurrence (n = 21 independent patient samples).
a, Heatmap of differentially expressed genes between pretreatment and post-treatment tumor (n = 11 independent paired patient samples). Differentially expressed genes identified using an FDR cut-off of P = 0.05 after adjusting for multiple comparisons. b, Heatmap and GEP score between patients with recurrence (n = 5 independent patient samples) and no recurrence (n = 8 independent patient samples). P value calculated using two-sided Mann–Whitney test. Error bar denotes mean ± s.d. c, GSEA of NRS genes that were enriched in TEFF, TMEM, and TEX versus TNAIVE cell signatures from ref. 19. d, Heatmap of angiogenesis-associated genes from gene ontology. e, Heatmap of B cell receptor-associated genes from gene ontology.
a, DFS of patients that recurred. b, CT image before and after of a patient with recurrent metastatic disease. c, Neoantigen load based on predicted binding (predicted kD of < 500 nM and mutant kD <wild-type kD). d, Number of high-quality neoantigens that are likely to be recognized by TCRs based on neoantigen fitness model42 at post-treatment versus recurrence time points.
About this article
Nature Reviews Clinical Oncology (2019)