Interferon-responsive HEVs drive tumor tertiary lymphoid structure formation and predict immunotherapy response in nasopharyngeal carcinoma

Abstract

The outcome of immune checkpoint blockade (ICB) therapy largely hinges on the antitumor immunity of tertiary lymphoid structures (TLSs), but drivers of tumor TLS formation remain exclusive. By integrating spatial transcriptomics and a pan-cancer single-cell atlas, we reveal the characteristics of TLSs in nasopharyngeal carcinoma (NPC) and identify a subset of interferon-responsive high endothelial venules (IFN-HEVs) that links to the emergence of tumor-specific chemokines, especially CXCL9. Functionally, CXCL9-secreting IFN-HEVs are associated with the recruitment of CXCR3⁺CD4⁺ T cells into TLSs. IFN-HEV-related phenotypes are strongly correlated with prolonged survival and enhanced ICB responsiveness. Leveraging these phenotypes, we develop a pretreatment CXCL9-TLS response-predictive scoring system (CTRscore), which robustly forecasts ICB therapeutic outcomes in three independent NPC cohorts. Our study provides biological and functional insights into the IFN-HEVs in tumor TLSs, highlighting their potential role in the development of biomarkers and predictors for the success of ICB therapy.

Keywords: CXCL9; high endothelial venules; immunotherapy; immunotherapy response; interferon; interferon-responsive high endothelial venules; nasopharyngeal carcinoma; prediction model; tertiary lymphoid structures.

Graphical abstract

Figure 1

Heterogeneity of TLSs from NPC and CN tissues (A) Schematic of the study design for illustrating NPC and CN TLSs using the GeoMx DSP technique. The diagram was created using BioRender. (B) The DEGs between TLSs from NPC and CN tissues. (C) GO categories of genes enriched in TLSs from NPC and CN tissues. (D) Comparison of putative cell type distribution within TLS ROIs from NPC and CN tissues. Bgc, germinal center B cell; Bm, memory B cell; CapEC, capillary-like endothelial cell; Fibro-CCL19, CCL19⁺ fibroblast; cDC, conventional dendritic cell; pDC, plasmacytoid dendritic cell; Tn-CD4, CD4⁺ naive T cell; Treg-CD4, CD4⁺ regulatory T cell; Tn-CD8, CD8⁺ naive T cell; Teff-CD8, CD8⁺ effector T cell; Tex-CD8, CD8⁺ exhausted T cell; T-proliferative, proliferative T cell. Two-sided unpaired Student’s t test or Wilcoxon rank-sum test was conducted, depending on whether the data passed the normality test. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001; not significant (n.s.), p > 0.05.

Figure 2

Identification of IFN-HEVs by pan-cancer vascular endothelial cell atlas (A) Uniform manifold approximation and projection (UMAP) plot of the Slingshot-derived VEC trajectory, depicting two major divergent trajectories. Trajectory 1 (T1) and trajectory 2 (T2) originated from VenEC-CLU (Clusterin) to ArtECs or TS-ECs, respectively. IFN-CapEC was the branchpoint of these two trajectories. VenEC-CLU, CLU⁺ venous endothelial cell; ArtEC, arterial endothelial cell; TS-EC, tip- and stalk-like endothelial cell; IFN-CapEC, IFN-activated capillary endothelial cell. (B) UMAP plot of ACKR1⁺RGCC⁺capillary venous (CV) endothelial cells from a pan-cancer scRNA atlas (NGDC: PRJCA018695), colored by cell types. CV1.Quiescent, quiescent CV; CV2.ESM1, ESM1⁺ CV; CV3.CDKN1A, CDKN1A⁺ CV; CV4.Intermediate.KLF4, KLF4⁺ intermediate CV; CV5.MFSD2A, MFSD2A⁺ CV; CV6.IGFBP5, IGFBP5⁺ CV; CV7.ISG15, ISG15⁺ CV; CV8.IFN-HEV.CXCL9, CXCL9⁺ IFN-HEV; CV9.HEV.IL33, IL33⁺ conventional HEV. (C) Distribution of CV subsets across CapEC-transition-1, IFN-CapEC, and CapEC-transition-2 clusters. CapEC-transition-1, transitional capillary-like EC-1; CapEC-transition-2, transitional capillary-like EC-2. (D) DEG analysis revealed distinct gene expression profiles among CV7.ISG15, CV8.IFN-HEV.CXCL9, and CV9.HEV.IL33. (E) Comparison of putative distribution of CV7.ISG15, CV8.IFN-HEV.CXCL9, and CV9.HEV.IL33 within TLS ROIs from NPC and CN tissues. Two-sided unpaired Student’s t test or Wilcoxon rank-sum test was conducted, depending on whether the data passed the normality test. ∗∗∗∗p < 0.0001; n.s., p > 0.05. (F) Diffusion component map indicating the differentiation order of tumor-enriched trajectory (T2). CapEC-transition-1, transitional capillary-like EC-1. (G) Radar plot showing the scaled activities of enriched REACTOME pathways in IFN-HEV and TS-EC. (H) Loess regression smoothened expression (y axis) of the representative genes of chemokine, IFN, collagen degradation, and VEGF signaling in pseudotime with the 95% confidence intervals (CIs). (I) GSEA plot for upregulated genes of tumor TLSs from the GeoMx dataset. NES, normalized enrichment score. p values were determined by a one-tailed permutation test by GSEA.

Figure 3

Transcriptomic and spatial characteristics of IFN-HEVs in tumor TLSs (A) Expression of HEV-common, CN-HEV-specific, TU-EC-specific, TU-HEV-specific, and tumor common genes in TU-HEVs, TU-ECs, CN-HEVs, and CN-ECs. TU-HEV, tumoral HEV; TU-EC, tumoral endothelial cell; CN-HEV, chronic nasopharyngitis HEV; CN-EC, chronic nasopharyngitis endothelial cell. IFN-stimulated genes, conventional HEV marker genes, and chemokines and chemokine receptors are presented. (B) Intersection of marker genes of NPC TLSs, IFN-HEVs, and TU-HEVs in GeoMx, pan-cancer single-cell, and RNA-seq datasets, respectively. (C) Representative flow cytometry plots for IFN-HEVs (CXCL9⁺MECA-79⁺) in NPC and CN tissues. (D) Quantification of HEVs and IFN-HEVs in NPC (n = 17) and CN (n = 9) tissues. Data are presented as mean ± SEM. Two-sided unpaired Student’s t test. ∗∗∗∗p < 0.0001; n.s., p > 0.05. (E) Representative mIHC images of IFN-HEVs and HEVs in TLSs of NPC and CN tissues, respectively. Scale bar, 50 μm. (F) Kaplan-Meier curves showing OS in patients of the internal NPC cohort (n = 105), stratified by the median of IFN-HEV signature. Log rank test. (G) Multivariate Cox regression analysis of OS in the internal NPC cohort (n = 105). Hazard ratios (HRs) and 95% CI for the IFN-HEV signature, gender, age, and clinical stage are shown. Two-sided p values from the Cox model are indicated.

Figure 4

CXCL9⁺ IFN-HEV correlates with the entry of CD4⁺ T cells into tumor TLSs (A) Representative images of tumor FFPE tissues showing TLS regions, IFN-HEV signature score, and the CD4, CD8A, and CD20 expression level of each spot from the spatial transcriptome dataset of clear cell renal cell carcinoma (ccRCC) (GEO: GSE175540). Only spots containing endothelial cells were included. (B) Correlation analyses between the IFN-HEV signature score and the expression level of CD4, CD8A, and CD20 (20019 spots, Pearson correlation test). Only spots containing endothelial cells were included. (C) IFN-HEV signature score between spots inside and outside the TLS region in the ccRCC spatial transcriptome dataset (GEO: GSE175540). Two-sided unpaired Wilcoxon rank-sum test. (D) Representative flow cytometry plots showing proportions of IFN-HEVs (CXCL9⁺MECA-79⁺), CD4⁺ and CD8⁺ T cells in NPC tissues (left). Correlations between IFN-HEV density and CD4⁺ T cell density or the CD4⁺/CD8⁺ T cell ratio in NPC tissues were concluded (right). n = 16, Pearson-correlation test. (E) Proportions of IL-4⁺, CXCR5⁺, T-bet⁺, IL-17⁺, and FOXP3⁺ CD4⁺ T cells in NPC tissues with low or high IFN-HEV infiltration (n = 5). Data are presented as mean ± SEM. Two-sided unpaired Student’s t test. ∗p < 0.05; ∗∗p < 0.01; n.s., p > 0.05. (F) Representative mIHC images of IFN-HEVs and CXCR3⁺CD4⁺ T cells in TLSs of the internal NPC FFPE cohort. Scale bar, 50 μm. (G) Average distance from CXCR3⁻CD4⁺ or CXCR3⁺CD4⁺ T cells to IFN-HEVs in the internal NPC FFPE cohort (n = 120). No detectable IFN-HEVs in 15 slides. Two-sided paired Student’s t test. ∗∗∗∗p < 0.0001. (H) Correlation analysis of density of IFN-HEVs and CXCR3⁺CD4⁺ T cells in our NPC FFPE cohort (n = 135). Pearson correlation test.

Figure 5

The prognostic value of IFN-HEVs and their effector CXCR3⁺CD4⁺ T cells within tumor TLSs (A) Kaplan-Meier analyses of PFS according to TLS density and features of IFN-HEVs and CXCR3⁺CD4⁺ T cells. Patients were divided into high- and low-infiltration groups (median cutoff); log-rank test. (B) Kaplan-Meier analyses of OS according to TLS density and features of IFN-HEVs and CXCR3⁺CD4⁺ T cells. Patients were divided into high- and low-infiltration groups (median cutoff); log-rank test. (C) Forest plot of univariate Cox regression for OS with patients’ clinical characteristics, TLS density, and features of IFN-HEVs and CXCR3⁺ CD4 T cells. HR and 95% CI are shown (HR < 1: association with longer survival; >1: with reduced survival). (D) Kaplan-Meier analyses of PFS (top) and OS (bottom) according to the univariate Cox regression model. Patients were divided into high- and low-risk groups (median cutoff); log-rank test.

Figure 6

Establishment of a pretreatment CXCL9-TLS response-predictive scoring system to predict the effect of immunotherapy (A) Representative mIHC images of IFN-HEVs and CXCR3⁺CD4⁺ T cells among NPC patients exhibiting complete response (CR), stable disease (SD), and progressive disease (PD) in response to ICB therapy.Scale bar, 50 μm. (B) Clinical characteristics and immunotherapy efficacy of the Sun Yat-Sen University Cancer Center (SYSUCC) training cohort (n = 30). (C) Frequency of patients exhibiting response (R) and non-response (NR) to ICB therapy, stratified on the best cutoffs of CXCR3⁺ cells, CD4⁺ cells, CXCL9⁺ cells, MECA-79⁺ cells, TLS density, CXCL9⁺MECA-79⁺ cells, CXCR3⁺CD4⁺ cells, and average distance between these cells. Patient numbers of each group are shown in brackets. Chi-square test. ∗p < 0.05, ∗∗p < 0.01. (D) Formula of CTRscore. CTRscore, CXCL9-TLS response-predictive scoring system. (E) Smoothed area under the curve (AUC)-receiver operating characteristic (ROC) plots for CTRscore, TLS density, CXCL9⁺MECA-79⁺ cells, CXCR3⁺CD4⁺ cells, and average distance between these cells in the SYSUCC training cohort (non-responders vs. responders). (F) Distribution of patients with high or low CTRscore (CTRscore-H/-L) in non-responders and responders of the SYSUCC training cohort (n = 30). (G) Clinical characteristics, immunotherapy efficacy, and distribution of patients with high or low CTRscore in non-responders and responders of the First Affiliated Hospital of Guangxi Medical University (GXMU) cohort (n = 34). (H) Clinical characteristics, immunotherapy efficacy, and distribution of patients with high or low CTRscore in non-responders and responders of the Sun Yat-sen Memorial Hospital, Sun Yat-sen University (SYSUMH) cohort (n = 29). (I) Clinical characteristics, immunotherapy efficacy, and distribution of patients with high or low CTRscore in non-responders and responders of the ShenShan Medical Center (SSMC) cohort (n = 20).