Abstract
Checkpoint blockade therapies that reactivate tumour-associated T cells can induce durable tumour control and result in the long-term survival of patients with advanced cancers1. Current predictive biomarkers for therapy response include high levels of intratumour immunological activity, a high tumour mutational burden and specific characteristics of the gut microbiota2,3. Although the role of T cells in antitumour responses has thoroughly been studied, other immune cells remain insufficiently explored. Here we use clinical samples of metastatic melanomas to investigate the role of B cells in antitumour responses, and find that the co-occurrence of tumour-associated CD8+ T cells and CD20+ B cells is associated with improved survival, independently of other clinical variables. Immunofluorescence staining of CXCR5 and CXCL13 in combination with CD20 reveals the formation of tertiary lymphoid structures in these CD8+CD20+ tumours. We derived a gene signature associated with tertiary lymphoid structures, which predicted clinical outcomes in cohorts of patients treated with immune checkpoint blockade. Furthermore, B-cell-rich tumours were accompanied by increased levels of TCF7+ naive and/or memory T cells. This was corroborated by digital spatial-profiling data, in which T cells in tumours without tertiary lymphoid structures had a dysfunctional molecular phenotype. Our results indicate that tertiary lymphoid structures have a key role in the immune microenvironment in melanoma, by conferring distinct T cell phenotypes. Therapeutic strategies to induce the formation of tertiary lymphoid structures should be explored to improve responses to cancer immunotherapy.
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Data availability
All relevant data are available and are included as Source Data. Digital spatial-profiling data used in Fig. 2 and gene-expression microarray data from Danish patients treated with anti-CTLA4 are available as Source Data. Data from public repositories were accessed from GSE65904 (ref. 23), TCGA data portal SKCM level 3 release 3.1.14.0, PRJEB23709 (ref. 22), https://github.com/riazn/bms038_analysis/tree/master/data, GSE115978 (ref. 18) and GSE120575 (ref. 17). Any other relevant data and code can be obtained from the corresponding authors upon reasonable request.
Change history
20 March 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-2155-6
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Acknowledgements
The study was supported by the Swedish Cancer Society, the Swedish Research Council, BioCARE, the Berta Kamprad Foundation, the King Gustaf V Jubilee foundation, Mats Paulsson’s foundation, Stefan Paulsson’s foundation and governmental funding for healthcare research (ALF). The Danish Cancer Society, the Aase and Einar Danielsen’s Fund and the Capital Region of Denmark Research Foundation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 641458.
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Contributions
G.J. conceived and supervised the study. R.C., M.L. and G.J. analysed and drafted text. R.C., B.P., K.L. and K.J. generated immunostaining data. R.C., A.S., I.J., B.P. and G.J. analysed immunostaining data. R.C., K.P. and G.J. generated and analysed immunofluorescence data. A.v.S. and S.W. generated digital spatial-profiling data. M.L. and G.J. analysed digital spatial-profiling data. R.C., M.L., S.M., K.H. and G.J. performed statistical analyses. M.L. and G.J. performed bioinformatic analyses. M.L. analysed scRNA-seq data. J.V.-C. generated RNA-seq data. M.L., A.S., M.D., M.S.L., I.J., B.P., K.H., J.V.-C., A.v.S., K.L., S.W., K.J., K.P., D.S., J.A.W. and G.J. interpreted data. M.D., M.S.L., H.O., C.I., K.I., H.S., L.B., A.C. and I.M.S. collected clinical specimens and clinical data. All authors approved and read the final draft.
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S.W and A.v.S. are employees of Nanostring Inc. and declare that there are competing interests. All other authors declare no conflict of interest.
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Peer review information Nature thanks James J. Mulé, Caroline Robert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Fig. 1 Characterization of TLSs in melanoma tumours.
a, CD20 (B cells), CD3 (T cells), CD8 (CD8+ T cells) and CD4 (CD4+ T cells) immunostaining in a representative melanoma with a TLS (n = 44 cases with TLS in the cohort of 177 cases). b, Subset survival analysis using CD8 and CD20 immunostaining in distant and lymph node metastases separately. n = 27 and 97 patients with available follow-up information, respectively. P values from Cox regression. c, Gene-expression characterization of the three groups using previously described signatures13. aDCs, activated dendritic cells; BVs, blood vessels; DCs, dendritic cells; IDCs, immature dendritic cells; LVs, lymph vessels; NK, NK cells; Tem, T effector memory cells; Tfh, T follicular helper cells; Tfh.Th2, T follicular helper 2 cells; Th, T helper cell; Th1, T helper 1 cell; Th2, T helper 2 cell. d, CD20, CD3, CD8, Ki67 and SOX10 immunostainings in three distant metastases. Arrows indicate the TLS. e, Survival analysis of 33 patients with TLS-containing tumours from regional lymph node metastases, stratified according to whether the TLS is located at the tumour border or is tumour-infiltrative. P value from Cox regression. f, Bar plot showing quantification of TLSs in tumours. Numbers in the box corresponds to TLSs per square millimetre. g, TLS gene score and type of lesion. n = 159 tumours. h, TLS score and immunological group. n = 159 tumours. In the box plots, the centre line represents the median, the box limits represent the lower and upper quartiles, and the whiskers extend to the most extreme values within 1.5× IQR. Numbers below the graphs represent numbers of patients.
Extended Data Fig. 2 High-plex proteomic analysis using the GeoMx assay and genomic characterization of tumours containing TLSs.
a, Workflow of the GeoMx assay. b, Immunofluorescence imaging of TLSs in tumour samples used in the GeoMx analysis. TLSs are sorted according to the unsupervised clustering of the high-plex proteomic data, performed on the different B cell populations. Pink, CD3+ T cells; green, tumour cells positive for PMEL and/or S100B; cyan, CD20+ B cells. For Ki67high 13 of 13 TLSs are displayed, and for Ki67low 15 of 17 TLSs are displayed. c, GeoMx data from 83 captured tumour cell regions. FDRs are from Kruskal–Wallis test, adjusted for multiple testing using the Benjamini–Hochberg method. d, Left, B2M immunostaining shows a significant difference between CD8/CD20 groups. P = 1× 10−11, Fisher’s exact test, n = 172 tumours). Right, plot shows B2M copy number status (blue = loss). P = 0.002, FDR adjustment for multiple comparisons = 0.007, Fisher’s exact test, n = 127 tumours. e–g, MHC-I (e) and MHC-II (f) expression (n = 160 tumours, P value from ANOVA) and mutational load (g) (n = 118 tumours, Kruskal–Wallis test) in relation to immunological groupings. h, Mutation heat map of melanoma-relevant genes in relation to immunological grouping. In the box plots, the centre line represents the median, the box limits represent the lower and upper quartiles, and the whiskers extend to the most extreme values within 1.5× IQR.
Extended Data Fig. 3 scRNA-seq analysis of tumour-associated B cells.
a, Box plots of gene-expression scores, based on different B cell developmental states in 812 B cells from 27 tumours from a previous study18. b, Heat map of selected genes across all 812 B cells. IGLL5and CD69 were two of the five genes with highest expression variation across all B cells. The heat map is sorted on IGLL5 and CD69 expression, excluding the cells that displayed increased expression of the plasma-cell signature. Genes showing a Pearson correlation >0.4 to IGLL5 or CD69 expression are also indicated. SDC1 and PRDM1 mark plasma cells, BCL6 and AICDA mark germinal centres, HLA-DRA mark MHC-II and HLA-A, HLA-B and HLA-C mark MHC-I. TLS-hallmark genes, germinal-centre-related genes and other B cell genes are also indicated. c, Extracting the single B cell RNA-seq data from a previous study17 using pretreatment samples (n = 16). The fraction of CD69+ B cells was higher in responders to ICB than in nonresponders (n = 8), but the fraction of IGLL5+ B cells was not. The fraction of IGLL5− CD69+ cells was also higher in responders. Plots of fraction of IGHD+ and IGHG+ B cells in relation to response to ICB therapy. P values from two-sided Wilcoxon test. In a, the centre lines in the box plot represent the median, the box limits represent the lower and upper quartiles, and the whiskers extend to the most extreme values within 1.5× IQR. d, Pearson correlation between expression of CD69 and germinal centre genes (CD83 and CXCR4) in data from a previous study18. Pie charts display the fact that the fraction of CD83+ and CXCR4+ B cells is increased among CD69+ B cells. Expression > 1 was used as a cut-off for being present. Seven hundred and fifty-three B cells without a present plasma-cell signature were analysed. P value from two-sided Fisher’s exact test. e, f, Heat map of gene-expression values corresponding to our TLS signature (e) and TLS-hallmark genes from the literature (f). Blue corresponds to increased expression. Mal., malignant cells. In e, f, single cells from the seven cell types on the left are from ref. 18, and from the four cell types on the right are from ref. 17.
Extended Data Fig. 4 Comparison of the derived TLS gene signature to other immune signatures.
a, Pearson correlation plots of the data from the cohort obtained at Skåne University Hospital, Lund (top, n = 160), data from cases of melanoma metastasis in the TCGA (bottom, n = 363) and baseline data from a previous publication22 (right, n = 69). Black box indicates the TLS signature. All signatures are taken from refs. 11, 19, 26. Red, positive correlation; blue, negative correlation. b, TLS gene-signature scores in primary tumours in comparison to distant and lymph node metastases. The number of tumours assigned to the TLShigh category is indicated above the plot.
Extended Data Fig. 5 TLS gene signature in cohorts treated by ICB.
a, Progression-free survival (PFS) and TLS gene signature in the Danish cohort of patients treated with anti-CTLA4. P value from Cox regression. b, TLS gene signature in relation to tumour mutational load, in data from a previous publication27 (n = 40 melanoma tumours). P value from Kruskal–Wallis test. c, Survival analyses on data from a previous study28, stratified according to whether patients are naive to anti-CTLA4 treatment or have progressed on anti-CTLA4. P values from Cox regression. d, Meta Cox regression analysis across the four cohorts treated using ICB (n = 186). P values from Cox regression adjusted for study. e, TLS gene signature of pretreatment (n = 16) and on-treatment samples (n = 10) in relation to therapy response in data from a previous publication30. P value from two-sided t-test. f, TLS gene signature of pretreatment (n = 38) and on-treatment (n = 39) samples in relation to RECIST response in data from a previous study28. P value from ANOVA test. g, TLS gene-signature score in 13 melanoma tumours that were also stained for CD20 protein. As an example, the tumour with the third highest score had TLSs. The two top tumours also had TLSs, whereas the other tumours did not. In the box plots in b, d, e, centre lines represent the median, the box limits represent the lower and upper quartiles, and the whiskers extend to the most extreme values within 1.5× IQR.
Supplementary information
Supplementary Data
Source Data 1: Microarray Affymetrix gene expression data derived from paraffin embedded tissue RNA. RNA was extracted from tumour blocks obtained from patients receiving anti-CTLA4 treatment as first-line therapy. Values are log2 transformed.
Supplementary Data
Source Data 2: Nanostring GeoMxTM high-plex proteomics data. Protein measurement of 60 immune proteins in T cells, B cells and tumor cells from melanoma tumors. Each value is normalized against IgG measurement.
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Cabrita, R., Lauss, M., Sanna, A. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020). https://doi.org/10.1038/s41586-019-1914-8
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DOI: https://doi.org/10.1038/s41586-019-1914-8
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