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Single-cell and spatial transcriptomics reveal mechanisms of radioresistance and immune escape in recurrent nasopharyngeal carcinoma

Nature Genetics volume 57, pages 1950–1965 (2025)Cite this article

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Abstract

Radiotherapy resistance and immune evasion are prominent features of recurrent nasopharyngeal carcinoma (rNPC). However, their mechanisms remain incompletely understood. Here, we conducted single-cell and spatial transcriptomics analysis of 39 tumors from 24 patients to reveal the microenvironmental differences between primary and rNPC. Specific MCAM+ cancer-associated fibroblasts are significantly enriched in rNPC, where they promote tumor radioresistance through the collagen IV–ITGA2–FAK–AKT axis. Furthermore, we found that collagen IV suppresses the infiltration of T cells, and we identified mechanisms of immune escape in rNPC. We uncovered the presence and function of CD8 ZNF683 cells in rNPC with lower cytotoxicity. The abundance of B cells and tertiary lymphoid structures significantly diminishes in rNPC. Finally, we confirmed that CD47–SIRPα commonly existed between myeloid and malignant cells in rNPC. This study provides an in-depth understanding of the mechanism of radioresistance and immune evasion in rNPC as well as highlighting critical preliminary targets for curing rNPC.

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Fig. 1: ScRNA-seq and spatial profiling of the pNPC and rNPC TME.
Fig. 2: Characteristics of malignant cells in PT and RT samples and their effects on the tumor ecosystem.
Fig. 3: MCAM-positive fibroblasts were identified in rNPC and were found to promote the radioresistance of NPC tumor cells.
Fig. 4: MCAM+ mCAFs promoted the radioresistance of NPC tumor cells through the COL4–ITGA2–FAK–AKT pathway.
Fig. 5: Intratumoral collagen IV levels were correlated with T cell suppression and immune escape in patients with NPC.
Fig. 6: CD8+ T cells maintain a memory phenotype with weak cytotoxicity in rNPC.
Fig. 7: TLSs were depleted in rNPC.
Fig. 8: SIRPα+ myeloid cells reside near CD47+ malignant cells and may inhibit antigen presentation in rNPC.

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Data availability

Raw scRNA-seq, spatial transcriptomics and WES data of pNPC and rNPC samples have been deposited in the Genome Sequence Archive of the Beijing Institute of Genomics (BIG) Data Center at the Beijing Institute of Genomics, Chinese Academy of Science, under accession number HRA010466 (accessible at http://bigd.big.ac.cn/gsa-human). Bulk RNA-seq data of human-NPC cell lines after indicated treatment were downloaded from the GSA-Human database under accession number HRA010688 (accessible at http://bigd.big.ac.cn/gsa-human). The WES data for 43 RT and 32 PT previously reported by our team are under accession numbers HRA000052 and HRA000053, which can be accessed at http://bigd.big.ac.cn/gsa-human. The bulk RNA-seq data from 88 PT samples reported in a previous publication26 are under accession number GSE102349. Source data are provided with this paper.

Code availability

The custom code used in this study is available at https://github.com/zhanglabtools/NPC_scRNA_Spatial_code. This repository has been archived at Zenodo (https://doi.org/10.5281/zenodo.15104454)78.

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Acknowledgements

Funding was provided by the National Natural Science Foundation of China (no. 82230034 to M.Y.C., 82373407 to R.Y., 82471134 to Y.P.L. and 82203231 to T.L.X.), the Guangzhou Science and Technology Plan Project (no. 202103010001 to M.Y.C.), the National Key Research and Development Program of China (no. 2021YFA1302500 to S.H.Z.), Guangzhou Science and Technology Plan Projects (no. 2023A04J1760 to T.L.X.) and Jiangxi Provincial Natural Science Foundation of China (no. 20212BDH80023 to M.Y.C.).

Author information

Author notes

  1. These authors contributed equally: Rui You, Qunlun Shen, Chao Lin, Kangning Dong, Xiao Liu, Hanshi Xu.

  2. These authors jointly supervised this work: Mingyuan Chen, Shihua Zhang, Tianliang Xia, Youping Liu, Rui You.

Authors and Affiliations

  1. Nasopharyngeal Cancer Center, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, The People’s Republic of China

    Rui You, Xiao Liu, Xiaoyi Song, Chunliu Huang, Youping Liu & Mingyuan Chen

  2. Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, The People’s Republic of China

    Rui You, Chao Lin, Hanshi Xu, Yulong Xie, Ruoqi Xie, Huifeng Li, Xiong Zou, Peiyu Huang, Yijun Hua & Mingyuan Chen

  3. State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, The People’s Republic of China

    Rui You, Chao Lin, Hanshi Xu, Wanming Hu, Yulong Xie, Ruoqi Xie, Jinhui Wu, Tao Yu, Huifeng Li, Zining Wang, Keming Chen, Xiong Zou, Peiyu Huang, Yijun Hua, Tianliang Xia & Mingyuan Chen

  4. State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, The People’s Republic of China

    Qunlun Shen & Shihua Zhang

  5. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, The People’s Republic of China

    Qunlun Shen & Shihua Zhang

  6. Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, The People’s Republic of China

    Chao Lin

  7. School of Mathematics, Renmin University of China, Beijing, The People’s Republic of China

    Kangning Dong

  8. Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, The People’s Republic of China

    Wanming Hu & Keming Chen

  9. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, The People’s Republic of China

    Shihua Zhang

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  1. Rui You
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Contributions

M.Y.C., S.H.Z., T.L.X., Y.P.L. and R.Y. conceived and designed the project and the strategy. R.Y., X.Z., P.Y.H., Y.J.H., Y.P.L. and M.Y.C. collected and created the samples. S.H.Z., Q.L.S., K.N.D., H.S.X., X.Y.S. and R.Q.X. designed and performed the computation and statistical analysis. C.L., X.L., C.L.H., Y.L.X., J.H.W., T.Y., H.F.L., Z.N.W., K.M.C., T.L.X. and R.Y. designed and performed the experiments. W.M.H. conducted the pathological evaluation. M.Y.C., S.H.Z., T.L.X., Y.P.L. and R.Y. supervised the study. All authors wrote, reviewed and edited the manuscript.

Corresponding authors

Correspondence to Rui You, Youping Liu, Tianliang Xia, Shihua Zhang or Mingyuan Chen.

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The authors declare no competing interests.

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Nature Genetics thanks Wei Dai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 ScRNA-seq and spatial profiling of the TME of pNPC and rNPC, related to Fig. 1.

(a) Histogram indicating the total number of detected genes (left panel) and the number of read counts (right panel). (b) Violin plot showing the expression levels of 8 known housekeeping genes in all cells. (c) UMAP plot for cells before integration, cells are colored according to the origin of patients (left panel), disease states (middle panel), and cell types (right panel). (d) Boxplot showing the acceptance rate calculated by kBET in uncorrected single cells and harmony-corrected ones. (e) Heatmap of the copy number variants (CNV) analysis, inferred from the scRNA-seq data. Each row indicates a single cell, NK and B cells were used as references. (f) Violin plot showing the CNV scores of inferred malignant cells and normal epithelial cells. Two-sided Wilcoxon rank-sum test. (g) UMAP plot showing the expression levels of marker genes, defined for major cell types. (h) Histogram indicating the ratio of cells in PT (blue) and RT (red) tissue samples. (i) Boxplot showing the number of detected genes of the indicated patients per spot across spatial data. (j-k) Count of programs in PT (blue) and RT (red) samples in Fig. 1f.

Extended Data Fig. 2 Characteristics of malignant cells in PT and RT samples, related to Fig. 2.

(a) Volcano plot shows differentially expressed genes (DEGs) between PT (blue) and RT (red) malignant cells, genes with top10 fold-changes are shown for each group. (b) Gene Set Enrichment Analysis (GSEA) and Gene Ontology of the DEGs between malignant cells from PT (blue) and RT (red) samples, based on the subontology of biological processes. (c) The proportion of significant interactions from fibroblasts to malignant cells compared with significant interactions from other immune cells to malignant cells inferred by LIANA. For fibroblasts, the proportion was calculated using the interaction number between fibroblasts (source) and tumor cells (target) as the numerator and the total interactions sent from fibroblasts as the denominator. For other immune cells, the proportion was calculated using the interaction number between other immune cells (source) and tumor cells (target) as the numerator and the total interactions sent from other immune cells as the denominator. Two-sided proportions z-test. (d) Schematic diagram illustrating the construction of radioresistant cell lines. (e) Representative images of the colony-forming assay of radiation-resistant (R, red) and wild type (W, blue) C666 and TW03 cell lines after being irradiated at doses of 0, 2, 4, 6, and 8 Gy. The right panel indicates the survival fractions of each cell line. Two-sided paired t-test. Scale bars, 5 mm. (f) The Spearman correlation between mutation load and the signature scores of four cancer cell-intrinsic transcriptional programs within PT and RT. Two-sided Spearman’s correlation test. (g) The Spearman correlation between mutation load and the ratios of various cell populations in TME within PT and RT. Two-sided Spearman’s correlation test. (h) Characterization of malignant cells and fibroblasts on spatial transcriptomics data using cell-type deconvolution. Scale bars, 500 µm. (i) Bar chart showing the number of significant L-R interactions from immune cells to malignant cells in PT (blue) and RT (red) samples.

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Extended Data Fig. 3 MCAM+ fibroblasts were identified in rNPC and promoted the radioresistance of NPC tumor cells, related to Fig. 3.

(a) Dotplot showing the normalized expression of marker genes belonging to the clusters defined in Fig. 3a. (b) Track plot showing the normalized expression of mCAF markers (ACTA2, POSTN) and the iCAF marker (CFD) in 4 CAF subclusters. Gene expression is represented by height. (c) Heatmap showing the normalized expression of MCAM and ACTA2 for each cell type. (d) Enrichment analysis of the top 50 DEGs based on the subtypes of CAFs, calculated using Metascape. (e) The primary fibroblasts were cultured from PT (left) and RT (right) samples. The images were taken by a microscope. Scale bars, 400 µm. (f) CAFs from PT and RT tissues were isolated and detected by flow cytometry. (g) MTT assay of NPC cell lines co-cultured with MCAM+ or MCAM− CAFs, with PBS as control. Two-sided paired t-test. (h) Representative immunofluorescent staining images show the expression of DAPI, Ki67, and MCAM in MCAM+ mCAFs and MCAM− CAFs. Scale bars, 50 µm. Two-sided t-test. (i) TW03 and C666 cells were co-cultured with MCAM+ mCAFs or MCAM− CAFs, after irradiation at the doses of 10 Gy, 15 Gy, and 20 Gy. The distribution of γ-H2AX foci was visualized by immunofluorescence staining. The cell nuclei were counter-stained with DAPI (blue). Scale bars, 20 µm. (j) Representative images of the colony-forming assay of NPC cell lines after being co-cultured with MCAM+ mCAFs or MCAM− CAFs, followed by different irradiation dose gradients (10 Gy, 15 Gy, and 20 Gy). Scale bars, 5 mm. (k) C666 cells (1 × 106) were injected alone or combined with MCAM+ or MCAM− CAFs (3:1) subcutaneously into the haunch of each nude mouse (n = 6 per group). After 6-8 days, all NCG mice were treated with local radiotherapy at a dose of 2 Gy for 5 consecutive days. (l) Representative IHC images immunofluorescent staining images show the expression of PANCK, DAPI, ACTA2, and MCAM in MCAM+ mCAFs and MCAM− CAFs. Scale bars, 50 µm.

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Extended Data Fig. 4 MCAM+ mCAFs promoted the radioresistance of NPC cells via the COL4-ITGA2-PI3K-AKT axis, related to Fig. 4.

(a) The expression of representative markers between MCAM+ and MCAM− mCAFs cells in the single cell (log (CPM + 1)) and bulk RNA seq cohort (log (FPKM + 1)). CPM, Counts Per Million; FPKM, Fragments Per Kilobase Million. (b-c) The percentage of apoptotic cells in the C666 cell line (b) and the TW03 cell line (c) was quantified by flow cytometry, treated with recombinant PDGFA or IGFBP7 protein, and irradiation at a dose of 10 Gy (n = 3 per group). Quantification represented by a bar plot (mean ± SD), right. Two-sided t-test. (d) Western blotting for the expression of COL4A1 in MCAM+ mCAFs and COL4A1KD MCAM+ mCAFs. (e) ELISA for type IV collagen in the supernatant of MCAM+ CAFs and COL4A1KD MCAM+ mCAFs, ng/ml (n = 3 per group). Two-sided t-test. (f) The correlation between the ratio of malignant cells and the expression of ITGA2 and ITGA3 based on RNA-seq data from Validation cohort 4. Two-sided Spearman’s correlation test. (g) Western blotting for the expression of COL4A1, COL4A2, vimentin, and α-SMA in MCAM+ mCAFs with or without knockdown COL4A1. (h) Representative images of the colony-forming assay of NPC cell lines after being treated with different concentration gradients of Collagen IV followed by irradiation (10 Gy) (n = 3 per group). Quantification represented by a bar plot (mean ± SD), right. Two-sided t-test. Scale bars, 5 mm. (i) Representative images of PANCK, ITGA2, ITGA3, and collagen IV in situ IHC staining in NPC samples. Scale bars, 50 µm. (j) Boxplot showing the expression of ITGA2 (left) and ITGA3 (right) in wide type (W, blue) and radioresistant type (R, Red) cell lines (n = 3 per group). One-sided paired t-test. (k) HEK293T cells were transfected with Flag-COL4A1, HA-ITGA2, or the related vector control and then subjected to immunoprecipitation and immunoblot with the designated antibodies.

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Extended Data Fig. 5 The formation of MCAM+ mCAFs was induced via TGF-β signaling, related to Fig. 4.

(a-b) Western blotting for key proteins of the FAK-AKT pathway in the indicated NPC cell lines, with or without collagen IV pretreatment (a), and with or without ITGA2 knockdown following collagen IV pretreatment (b). (c) TW03 and C666 cells were treated with or without the AKT inhibitor MK2206 in the presence of type IV collagen. After irradiation at a dose of 10 Gy, the distribution of γ-H2AX foci was visualized by immunofluorescence staining (n = 3 per group). The cell nuclei were counter-stained with DAPI (blue). Scale bars, 20 µm. (d) Volcano plot shows DEGs between C666 bulk samples (left) and RT sh-ITGA2 C666 bulk sample (right). The names of the reported irradiation-resistance related TFs are indicated in the plot1-6. (e) Correlation between the expression of TFs which highly expressed in C666 bulk samples and the cell score for FAK-PI3K_AKT in single malignant cells. Two-sided Spearman’s correlation test. (f) Enrichment of the TFs that are highly expressed in C666 bulk samples (versus sh-ITGA2 C666). (g) C666 cells combined with MCAM+ mCAFs (3:1) were injected subcutaneously into NCG mice (n = 6 per group). The mice were randomly subjected to gavage with MK2206 or DMSO, and treated with radiotherapy at a dose of 2 Gy for 5 consecutive days. (h) The deconvolution score of malignant and the score of the TGF-β pathway in malignant spots based on the ST data. Scale bars, 500 µm. (i) Violin plots showing the TGF-β score in malignant cells from PT (blue, n = 3,241) and RT (red, n = 11,105) samples based on the scRNA-seq data. Two-sided Wilcoxon rank-sum test. (j) Boxplot showing the TGF-β scores of NPC cells from wild type (W) and radioresistant tumor cells (R) based on the bulk RNA seq data (n = 3 per group). One-sided paired t-test. (k) Western blotting for key proteins in MCAM− CAFs after being treated with recombinant TGF-β in different concentration gradients. (l) MTT assay was performed after MCAM− mCAFs cells were treated with increasing doses of TGF-β. Two-sided ANOVA test.

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Extended Data Fig. 6 The construction and validation of HuHSC-NCG mice, related to Fig. 5.

(a) Schematic illustration of the construction process of the humanized mouse model. (b) After establishing the humanized mouse model, the percentage of mCD45 cells, hCD45 cells, hCD19 cells, and hCD3 cells among PBMC was detected in 18 of the mouse models by flow cytometry. (c) Western blotting for the expression of Col4a1 in MCAM+ CAFs and Col4a1 knockout MCAM+ CAFs. (d) Scatter plot showing the correlations between the expression level of COL4A1 and the ratio of CD8+ T, and CD4+ T cells in spatial data. Two-sided Spearman’s correlation test. (e) C666 cells (1×106) were combined with MCAM+ mCAFs with or without COL4A1 KO and injected subcutaneously into HuHSC-NCG mice followed by irradiation (n = 5 per group). Box plot showing the ratio of CD8+T cells and subgroups of CD8+ T cells in the tumor mice using flow cytometry. Two-sided t-test.

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Extended Data Fig. 7 CD8+ T cells maintain a memory phenotype with weak cytotoxicity in rNPC, related to Fig. 6.

(a) Heatmap showing the expression of marker genes for each cluster defined in Fig. 6a. (b) Heatmap indicating the expression of selected gene sets in T/NK subtypes, including naive, resident, inhibitory, cytokines, co-stimulatory, transcriptional factors (TF), and cell type. (c) Boxplots illustrating the ratios of NK subtypes in PT (blue) and RT (red) tumors, respectively. Two-sided Wilcoxon rank-sum test. (d) UMAP plot showing the gene sets for CD8+ T cells, including naive, cytotoxic, and exhausted. (e) Tree map showing the correspondence between the subtypes of CD8+ T cells and other functional CD8+ T cell types of including naive, cytotoxic, and exhausted. (f) Pseudotime-ordered analysis of CD8+ T cells from pNPC and rNPC samples. UMAP plots are labeled by pseudotime (left panel), as well as the naive, cytotoxic, and exhausted CD8+ cell types (right panel). (g) Negative gates of ZNF683 and granzyme B in FACS assays.

Extended Data Fig. 8 Tertiary lymphoid structures are depleted in rNPC, related to Fig. 7.

(a-b) Violin plots showing the percentages of various immune-cell types in TLS (green) and other tissues (orange) in two representative patients. Two-sided Wilcoxon rank-sum test. (c-d) Violin plots showing the percentages of B, CD4+ T, and CD8+ T cell subclusters in TLS (green) and other tissues (orange) in two representative patients. Two-sided Wilcoxon rank-sum test. (e-f) Kaplan-Meier analysis showing the PFS of patients from validation cohort 4, stratified by low (green) or high (blue) expression of MS4A1 (n) and BANK1 (o). Two-sided log-rank test. (g) Kaplan-Meier analysis showing the PFS of patients from validation cohort 4, stratified by low (green) or high (blue) ratio of B cells. Two-sided log-rank test.

Extended Data Fig. 9 SIRPα+ myeloid cells reside near CD47+ malignant cells to inhibit antigen-presentation in rNPC, related to Fig. 8.

(a) UMAP plot showing myeloid cells, color-coded according to PT (blue) and RT (red). (b) t-SNE plot showing the expression of marker genes for the identification of the indicated cell subtypes in Fig. 8a. (c) Scatterplots showing M1 and M2 scores (left panel), as well as pro- and anti-inflammatory scores (right panel) for each color-coded macrophage subtypes. (d) Colocalization scores of TME cells to malignant cells in PT (left) and RT (right). (e) Differences in colocalization scores of TME cells to malignant cells between PT and RT samples. (f) Bar chart showing the number of significant LR interactions from malignant to myeloid subtypes in PT (blue) and RT (red) samples. (g) Scatterplot showing the association between the expression of SIRPA in myeloid subtypes and the distance of these subclusters to CD47+ malignant cells in PT samples, inferred from spatial transcriptomics (ST). Two-sided Spearman’s correlation test.

Extended Data Fig. 10

Schematic diagram of the mechanism of radioresistance and immune escape in rNPC and potential targeted treatment strategies.

Supplementary information

Supplementary Information

Supplementary Notes 1–13, Supplementary Figs. 1–4 and References.

Reporting Summary

Peer Review File

Supplementary Tables 1–7

1. Clinical information for cohort. a, Clinical information of patients in discovery cohort 1. b, Clinical information of patients in validation cohort 1. c, Clinical information of patients in validation cohort 2. 2. All the relevant gene sets used in the current study. 3. Multivariate analysis of the proportions of MCAM+ mCAFs and other clinical characteristics in LRRFS and overall survival. 4. Communication P value and neighborhood enrichment score on spatial transcriptomics data. 5. The proportions and count of leukocytes in the humanized mouse model. 6. The comparison of the percentages of cell types and subtypes in TLS versus other tissues. 7. Relevant sequence used in this article. a, The RT–qPCR sequence for Notch3, Sox4, Atf3, Col4a1, Sox9, and Mcam. b, The siRNA sequence for Notch3, Sox9, Atf2, Atf3, Notch3, Sox4. c, The shRNA sequence for Itga2, Col4a1.

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You, R., Shen, Q., Lin, C. et al. Single-cell and spatial transcriptomics reveal mechanisms of radioresistance and immune escape in recurrent nasopharyngeal carcinoma. Nat Genet 57, 1950–1965 (2025). https://doi.org/10.1038/s41588-025-02253-8

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