Abstract
Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.
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Data availability
The ST and scRNA-seq data that support the findings of this study have been deposited in the National Genomics Data Center, China under accession codes HRA004677 and CRA011176. To protect individual privacy, the raw human sequencing data are subject to controlled access. The processed data that supports the findings of this study are available in Figshare at https://doi.org/10.6084/m9.figshare.22434341 or in the National Genomics Data Center under accession code OMIX003593. Source data for Figs. 1–7 and Extended Data Figs. 1–10 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability
This study did not generate custom code for the analyses. Standard workflows and open-source R packages and software were used (Methods).
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Acknowledgements
We thank Q. Xie (West Lake University), B. Peng (Fudan University), X. Ye (Sun Yat-sen University) and W. Lin (Sun Yat-sen University) for their kind help and discussion. This work was supported by the following grants: the National Key R&D Program of China (2019YFA0110300 and 2020YFA0509400 to J.C.), the National Natural Science Foundation of China (82203538 to Y.M.; 82150117, 82071745 to J.C.; 82130076, 81972668 to Q.L.; 31900570 to G.J.; and 82101329 to W.Y.), the start-up fund of Nanshan Scholarship of Guangzhou Medical University (to G.J. and Q.L.), Guangzhou Key Medical Discipline Construction Project Fund (to Q.L.), High-level Hospital Construction Project (DFJHBF202108 and YKY-KF202204 to Q.Z.), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (2022B1212010011 to Q.Z.) and the Guangdong Project (2019QN01Y212 to J.C.).
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Contributions
G.J., J.C., Q.L., Q.Z. and J.Z. conceived and coordinated the project. Y.M. performed scRNA-seq, ST and most human sample experiments. Xiumei Wang carried out most mouse experiments. Y.M. and H.M.B. prepared the patient samples. G.J., J.H., D.L, X.L., X.R., L.Q. and X.C. performed bioinformatics analysis. C.H., Y.F., Y.W., Xinyu Wang, Y.L. and L.W. helped with animal experiments. J.L. and W.Y. provided guidance for mouse experiments and data analysis. H.L. was responsible for laboratory animals. Z.L., Y.H. and L.C. assisted with scRNA-seq and ST experiments. D.L. and Y.H. assisted with manuscript and figure preparation. G.J., J.C., Q.L., Q.Z. and J.Z. interpreted the data. G.J. and J.C. wrote the manuscript.
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Nature Cancer thanks Jennifer Guerriero, Tobias Weiss and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 The neoadjuvant treatment timeline and MRI of patients and the schematic illustration of scRNA-seq and ST analyses.
(a) Schematic of the neoadjuvant treatment. i.v.gtt, intervenous drop infusion. q.d., once a day. See also (Methods). (b and c) Magnetic resonance imaging (MRI) of responsive (Pt18) and nonresponsive (Pt2) GBM patients before and after neoadjuvant therapy. OP, operation. See also Fig. 1a. (d) Schematic of the experimental design for scRNA-seq and ST analyses of human GBM. ST, spatial transcriptomics. (e) Dot plot showing the expression of canonical marker genes in major immune and nonimmune cell types analyzed by scRNA-seq in Fig. 1c. (f) Cell proportions of major immune and nonimmune cell types from 24 tumor samples (5 ND, 7 Rec, 5 nonresponders and 7 responders) analyzed by scRNA-seq.
Extended Data Fig. 2 Expression heterogeneity in the paratumor and tumor core regions of human GBM determined by ST.
(a) Heatmap depicting pairwise correlations of the indicated expression programs derived from 34 ST datasets (18 paratumor sections and 16 tumor core sections). Clustering identified six coherent expression programs across the ST sections. (b) Expression scores of gene sets analyzed by ST for representative samples. Non-malignant and malignant cells are indicated according to the CNV analysis results. See also Supplementary Fig.s 1 and 2. (c) Plots showing hypoxia, immune inflammatory and angiogenesis scores for ST spots in paratumor. Box limits represent the first quartile and third quartile, the line inside the box represent s the median, and the whiskers extend from the box to show the 5th percentile to the 95th percentile (n = 44,629 spots over 34 samples from 18 patients). Two-sided Student’s t-test. (d) Volcano plot showing differentially expressed genes [−log10(adjusted P) > 5, log2(FC) > 0.5] in the tumor core vs. paratumor regions in human GBM.
Extended Data Fig. 3 T cells in human GBM after neoadjuvant anti-PD-1 therapy.
(a) UMAP plot of 27,607 T cells from 24 tumor samples (5 ND, 7 Rec, 5 nonresponders and 7 responders) analyzed by scRNA-seq. (b) Distribution preference of each T-cell cluster across ND (n = 5), Rec (n = 7), nonresponder (n = 5) and responder (n = 7) tumor samples estimated from the observed-to-expected ratio (Robs/exp) (see also Methods). (c) UMAP plot of CD4+, CD8+, proliferative and stressed T-cell clusters. (d) UMAP plots showing the expression levels of selected genes in (c). (e) Dot plot showing the marker genes of CD4+ T-cell clusters. (f and h) The proportions of Tregs (C10) (f) and Th1-like cells(C14) (h) across ND (n = 5), Rec (n = 7), nonresponder (n = 5) and responder (n = 7) tumor samples. Data are presented as the mean ± SEM. Two-sided fisher’s exact test. (g and i) Heatmap showing the expression of selected genes in Tregs (C10) (g) and Th1-like cells (C14) (i) Rows represent genes, and columns represent the averaged expression of those genes. (j) Expression of marker genes for CD8+ T-cell clusters. (k) UMAP plots showing CD8+ T-cell clusters. (l) Proportion of each CD8+ T-cell type in all patients (n = 24 patients). Data are presented as the mean ± SEM. P values were determined by a two-sided Dirichlet-multinomial regression model. (m) Smoothed heatmap showing the expression of selected genes in Texp and Tex cells. The columns are aligned along the pseudotime trajectory. (n) Dot plot showing selected genes of CD8+ T-cell clusters. (o) The expression levels of the indicated genes in CD8_CD160 (C9) across ND (n = 5), Rec (n = 7), nonresponder (n = 5) and responder (n = 7) tumor samples. Box limits represent the first quartile and third quartile, the line inside the box represents the median, and the whiskers extend from the box to show the 5th percentile to the 95th percentile. Two-sided Student’s t-tests. n.s., not significant.
Extended Data Fig. 4 TAMs in human GBM after neoadjuvant anti-PD-1 therapy evaluated by scRNA-seq.
(a and b) Proportions of microglia (C1) (a) and CXCL10+ microglia (C8) (b) across ND (n = 5), Rec (n = 7), nonresponder (n = 5) and responder (n = 7) tumor samples. Data are presented as the mean ± SEM. Two-sided fisher’s exact test. n.s., not significant. (c) Comparison of the average expression of each gene in CXCL10+ microglia (C8) (y-axis) versus microglia (C1) (x-axis). Red points indicate selected significantly upregulated genes in CXCL10+ microglia (C8) with an adjusted P < 0.01 (One-sided wilcoxon rank-sum test). (d) Boxplot showing the ROUGE index of each subset across 24 patients. (e) The developmental trajectory of all monocytes, macrophages and microglia inferred by RNA velocity analysis. Arrows indicate the orientation of the inferred developmental pseudotime trajectory. UMAP plot showing individual cell cluster. Heatmap showing smoothened expression of selected genes in SIGLEC9+SEPP1+ TAMs (C2), SIGLEC9+MARCO+ TAMs (C9) and CD14+ monocytes (C3). The columns represent individual genes and are aligned along the pseudotime trajectory. (f) Volcano plot showing differentially expressed genes of SIGLEC9+SEPP1+ TAMs (C2) in Rec vs ND groups. (g) Violin plot showing selected genes in published dataset.
Extended Data Fig. 5 Spatial transcriptomics of TAMs in human GBM, and Siglec-9 expression in tissue microarray including 159 glioma patients.
(a) Dot plot showing the proportions of deconvoluted ST cell types compared to reference scRNA-seq TAM cell types. Ideally each reference cell type showed the unique deconvoluted ST cell types. (b) The proportion of deconvoluted ST cell types in each spot. (c) SIGLEC9 gene expression in major cell subpopulations of human GBM tumor samples analyzed by scRNA-seq. (d) The spatial distributions of SIGLEC9+SEPP1+ TAMs and SIGLEC9+MARCO+ TAMs in responders and nonresponders. (e and f) Statistics for (c) showing the proportions of SIGLEC9+SEPP1+ TAMs and SIGLEC9+MARCO+ TAMs in responders and nonresponders (d) or paratumor and tumor core regions (e) in all myeloid spot (CD68+) (n = 5,839 spots over 5 patiens). Two-sided wilcoxon rank-sum test. (g) Overall staining of Siglec-9 in the tissue microarray. The microarray contained 180 samples and one control spot, and the detached spots (red circles) were excluded, leaving 159 samples retained for analysis. (h) Representative staining of high, moderate, low and negative immunostaining for the Siglec-9 protein in GBM tissues. Images are representative of 159 images of one tissue microarray chip.
Extended Data Fig. 6 Deletion of Siglece impeded subcutaneous tumor growth by CT2A and GL261 GBM cells.
(a) Generation of the Siglece knockout mouse strain. The coding region spanning exons 1-7 was deleted by CRISPR/Cas9-mediated gene editing. (b) Additional representative histology of mouse brain with intracranial tumors generated following inoculation of CT2A cells. Images are representative of at least 3 images across one indicated animal. Scale bar, 2 cm. (c, d, e, and f) The subcutaneous tumor volume (c and e) and overall survival (d and f) of WT and Siglece−/− mice inoculated with CT2A (c and d) or GL261 (e and f) tumor cells. The numbers of mice are as indicated. Data are presented as the mean ± SEM (c, e). Box limits represent the first quartile and third quartile, the line inside the box represents the median, and the whiskers extend from the box to show the 5th percentile to the 95th percentile. Two-sided log-rank test. (g) The overall survival of WT and Siglece−/− mice bearing subcutaneous tumors treated with anti-CSF1R antibody or left untreated following inoculation of CT2A cells. The numbers of mice are as indicated. Two-sided log-rank test. n.s., not significant. (h) Dot plot showing the expression of canonical marker genes in major immune and nonimmune cell types as analyzed by scRNA-seq in Fig. 5a. (i and j) Pathways enriched in macrophages (i) and T cells (j) from intracranial CT2A GBM tumors in Siglece−/− mice compared with those in WT mice by Gene Ontology analysis. P values were calculated from the hypergeometric distribution. (k) Heatmap showing the expression of all significant genes in subcutaneous CT2A tumors in Siglece−/− mice (n = 3) vs. those in WT mice (n = 3) as analyzed by bulk RNA-seq. (l) Gene Ontology analysis of (a). P values were calculated from the hypergeometric distribution. (m) Heatmap showing the expression of selected genes analyzed by RNA-seq.
Extended Data Fig. 7 Deletion of Siglece activated BMDMs, CD4+ T cells and CD8 + T cells.
(a) The indicated gene levels measured by FACS analysis in BMDMs. LPS, lipopolysaccharide. (b) Surface expression of α2,3-linked or α2,6-linked sialic acids on CT2A glioma cells that was treated with neuraminidase at indicated concentration or left untreated. Panel shows a representative experiment. (c) Siglec-E levels on BMDMs cultured with or without CT2A cell-conditioned medium that was treated with neuraminidase or left untreated. BMDMs were isolated from WT mice (n = 5). (d and e) Proportions of proliferating OT-II (CD4+) (d) and OT-I (CD8+) (e) T cells stimulated with ova peptide-pulsed splenic BMDCs from WT (n = 3) or Siglece−/− (n = 3) mice. (f and g) The indicated gene levels measured by FACS analysis of intratumoral (f) or splenic (g) T cells. The cells were isolated from WT (n = 3) or Siglece−/− (n = 3) mice bearing intracranial CT2A glioma. (h and i) The overall survival of WT and Siglece−/− mice bearing subcutaneous tumors treated with anti-CD4 (h) or anti-CD8 (i) antibodies or left untreated following inoculation of CT2A cells. (j) The graphical representation of the transwell assay to measure T cell migration. (k and l) Cell migration under attraction of cytokines in CD4+ T cell (k) or CD8+ T cell (l) was evaluated. Each symbol represents one independent assay. (m) Expression scores of the gene sets of responders and non-responders analyzed by ST. See also Fig. 6e. (n) CD44+ intratumoral T cells measured by FACS analysis. The cells were isolated from WT (n = 9 mice for CD4, n = 6 mice for CD8) and + (n = 8 mice) bearing intracranial CT2A glioma. (o and p) The indicated gene levels measured by FACS analysis of intratumoral T cells (o) and splenic T cells (p). The cells were isolated from WT (n = 3) or Siglece−/− (n = 3) mice bearing intracranial CT2A glioma. Data are summarized from three independent experiments (a-p). Bar plots are means ± SEM (a-p). Two-sided Student’s t-test (a-p).
Extended Data Fig. 8 Identification of malignant cells in human GBM.
(a) Large-scale CNVs of radial glia, OPCs, astrocytes and oligodendrocytes selected from scRNA-seq data for human GBM. The non-malignant cell type pDCs were used as the baselines (reference cells) to estimate the CNVs of malignant cells. The X and Y chromosomes were omitted. (b) UMAP plot of selected nonimmune cell subpopulations (pDCs were selected as the non-malignant cell reference) (left) and inferred malignant and non-malignant cells (right). (c) Box plots showing gain of chromosome 7 and loss of chromosome 10 (the most frequent CNVs in GBM) in inferred malignant and non-malignant cells (n = 4,6694 cells over 24 patients). Box limits represent the first quartile and third quartile, the line inside the box represents the median, and the whiskers extend from the box to show the 5th percentile to the 95th percentile. Two-sided wilcoxon rank-sum test. (d) Normalized expression levels of ST3GAL4 in non-malignant cells and malignant cells from 24 tumor samples (5 ND, 7 Rec and 12 Neo) analyzed by scRNA-seq. Two-sided wilcoxon rank-sum test. (e) The correlation of averaged expression between ST3GAL4 and SIGLEC9 in non-malignant cells and malignant cells in patients based on scRNA-seq data. Spearman rank correlation. (f) Bubble heatmap showing the interactions for selected ligand-receptor pairs between SIGLEC9+ TAMs and non-malignant or malignant cells analyzed by scRNA-seq analysis of human GBM data. P values were generated by permutation test.
Extended Data Fig. 9 The absence of Siglece enhances the efficacy of ICB therapy in murine GBM model.
(a) Schematic illustration of ICB therapy. WT or Siglece−/− mice were subcutaneously inoculated with CT2A glioma cells and the mice bearing tumors were administered intraperitoneally with anti-mouse PD-1, anti-mouse PD-L1 or left untreated on days 12, 16, 20, and 24. Tumor growth was monitored. (b, c, d and e) The subcutaneous tumor volume (b, d) and overall survival (c, e) of WT and Siglece−/− mice treated with anti-PD-1 (b, c), anti-PD-L1 (d, e) or left untreated after inoculation of CT2A cells. The numbers of mice are as indicated. Data are means ± SEM (b, d). Two-sided log-rank test. (f) Schematic illustration of Siglec-E-Fc therapy. WT mice were subcutaneously inoculated with CT2A glioma cells and the mice bearing tumors were administered intraperitoneally with Siglec-E-Fc or IgG. Tumor growth was monitored. (g) The subcutaneous tumor volume of WT mice treated with Siglec-E-Fc or IgG after inoculation of CT2A cells. The numbers of mice are as indicated. Data are means ± SEM. Two-sided log-rank test.
Extended Data Fig. 10 Schematic illustration of macrophage specialization and T cell interactions in human and mouse GBM models following various treatment regimens.
Recurrent GBM tumors are associated with angiogenesis, loss of microglial cells, and progressively increased infiltration of monocyte-derived TAMs. SIGLEC9+ TAMs persist after neoadjuvant anti-PD-1 therapy, especially in non-responders. Targeting Siglec-E (murine homolog of Siglec-9) in mouse GBM models on macrophages priorly activates T cells via secretion of chemokines, interaction with co-stimulation factors and upregulation of IFN-γ-related pathways thereby enhancing the anti-PD-1/PD-L1 therapy efficacy. Thus, Siglec-9/Siglec-E represents an immune checkpoint on macrophages and is a potential target for cancer immunotherapy.
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Mei, Y., Wang, X., Zhang, J. et al. Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response. Nat Cancer 4, 1273–1291 (2023). https://doi.org/10.1038/s43018-023-00598-9
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DOI: https://doi.org/10.1038/s43018-023-00598-9
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SIGLEC9 tips the myeloid balance in glioblastoma
Nature Cancer (2023)
