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Sirpα on tumor-associated myeloid cells restrains antitumor immunity in colorectal cancer independent of its interaction with CD47

Nature Cancer volume 5, pages 500–516 (2024)Cite this article

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Abstract

Immunosuppressive myeloid cells hinder immunotherapeutic efficacy in tumors, but the precise mechanisms remain undefined. Here, by performing single-cell RNA sequencing in colorectal cancer tissues, we found tumor-associated macrophages and granulocytic myeloid-derived suppressor cells increased most compared to their counterparts in normal tissue and displayed the highest immune-inhibitory signatures among all immunocytes. These cells exhibited significantly increased expression of immunoreceptor tyrosine-based inhibitory motif-bearing receptors, including SIRPA. Notably, Sirpa−/− mice were more resistant to tumor progression than wild-type mice. Moreover, Sirpα deficiency reprogramed the tumor microenvironment through expansion of TAM_Ccl8hi and gMDSC_H2-Q10hi subsets showing strong antitumor activity. Sirpa−/− macrophages presented strong phagocytosis and antigen presentation to enhance T cell activation and proliferation. Furthermore, Sirpa−/− macrophages facilitated T cell recruitment via Syk/Btk-dependent Ccl8 secretion. Therefore, Sirpα deficiency enhances innate and adaptive immune activation independent of expression of CD47 and Sirpα blockade could be a promising strategy to improve cancer immunotherapy efficacy.

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Fig. 1: Tumor-enriched myeloid cells exhibit the highest immune-inhibitory signatures.
Fig. 2: Sirpα deletion inhibits solid tumor development.
Fig. 3: Sirpα deficiency prohibits spontaneous colon cancer development.
Fig. 4: Sirpα deficiency reprograms tumor immune microenvironment to facilitate antitumor immunity.
Fig. 5: Sirpα deficiency enhances macrophage function and T cell activation.
Fig. 6: Sirpα deficient macrophages enhance T cell recruitment via Syk/Btk-dependent Ccl8 secretion.

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Data availability

The RNA-seq and scRNA-seq data that support the findings of this study have been deposited in the Genome Sequence Archive75 in the National Genomics Data Center76, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (CRA005626 and HRA001948, with processed data available in OMIX921) that are accessible at https://ngdc.cncb.ac.cn. Access of sequence data of human samples is restricted due to patient privacy and China’s human genetic resources regulations. HRA001948 can be accessed by clicking the ‘Request Data’ button and filling in the required information for approval. The public scRNA-seq data are available in GEO (GSE188711 and GSE66343) and Synapse (syn26844071), and the public RNA-seq data are available in GEO (GSE17536 and GSE17537) and TCGA (TCGA-LIHC and TCGA-LUSC). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

The custom script and exact parameters used for data processing and analysis are available in GitHub at https://github.com/snow55/SIRPAproject.

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Acknowledgements

This study was supported by grants from National Key R&D Program of China (2020YFA0509400 to J.C.; 2021YFF1200900 to W.J. and N.H.; 2019YFA0110300 to J.C.; and 2021YFA0909300 to W.J. and N.H.), National Natural Science Foundation of China (82150117 and 82071745 to J.C.; 32170646 and 32370688 to W.J.; 82101329 to W.Y.; and 32300760 to C.H.), the Guangdong project (2019QN01Y212 to J.C. and 2023B1111020006 to W.J.), Guangdong Basic and Applied Basic Research Foundation (2023A1515011908 to N.H.) and Shenzhen Science and Technology Program (JCYJ20220818100401003 and KQTD20180411143432337 to W.J.). We thank the Guangdong Engineering & Technology Research Center for Disease-Model Animals, Laboratory Animal Center, Zhongshan School of Medicine for support in all animal experiments. We thank members of the Chen laboratory and Jin laboratory for assistance and discussion.

Author information

Author notes

  1. These authors contributed equally: Chunliu Huang, Xuefei Wang, Yingzhao Wang.

Authors and Affiliations

  1. Molecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Zhuhai, China

    Chunliu Huang & Shoudeng Chen

  2. Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Chunliu Huang, Yingzhao Wang & Weiling He

  3. School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Xuefei Wang, Shan Chen, Peidong Yan, Qi Zhang, Ni Hong & Wenfei Jin

  4. Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Yongyi Feng, Xiumei Wang, Lixiang Wang, Xinyu Wang & Jun Chen

  5. GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China

    Jing Liao

  6. Department of Anatomy and Histology, Shenzhen University Health Science Center, Shenzhen, China

    Chengzhou Mao

  7. Shenzhen People’s Hospital, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China

    Yang Li

  8. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China

    Wei Yi

  9. Guangdong Engineering & Technology Research Center for Disease-Model Animals, Laboratory Animal Center, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Weibin Cai & Jun Chen

  10. Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Weibin Cai

  11. Department of Gastrointestinal Surgery, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China

    Weiling He

  12. Key Laboratory of Tropical Disease Control of the Ministry of Education, Sun Yat-sen University, Guangzhou, China

    Jun Chen

  13. Jinfeng Laboratory, Chongqing, China

    Jun Chen

  14. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Wenfei Jin

Authors
  1. Chunliu Huang
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  2. Xuefei Wang
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  6. Shan Chen
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Contributions

W.J. and J.C. conceived the project. Y.L. and Q.Z. collected the clinical samples. S.C. and P.Y. conducted the scRNA-seq. Xuefei Wang analyzed the data. C.H. and Y.W. performed the experiments, with contributions from Y.F., Xiumei Wang, J.L., C.M., L.W., Xinyu Wang, W.Y., W.C. and S.C. N.H., W.H., J.C. and W.J. supervised the study. J.C. and W.J. wrote the manuscript, with input from other authors. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Ni Hong, Weiling He, Jun Chen or Wenfei Jin.

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Extended data

Extended Data Fig. 1 Myeloid cells demonstrate pro-tumor signatures in tumor tissues of colorectal cancer patient.

a. t-SNE visualization of cell type specific genes colored by expression level. b. Cell proportion of each cell type in CRCN, CRCP and CRCT tissues calculated by scDC. The p value were calculated by bootstrap resampling through GLM model in scDC. c. Heat map of top 3 differentially expressed genes in each myeloid cell cluster. Color represents scaled gene average expression in each cluster. d, e. Volcano plot of differentially expressed genes in macrophage (d) and neutrophil (e) between tumor tissue and normal/paracancerous tissue (Wilcoxon test, two-sided). Red points and blue points represent tumor tissue highly expressed genes and normal/paracancerous tissue highly expressed genes, respectively. Bonferroni was used for correction of multiple comparisons.

Source data

Extended Data Fig. 2 Tumor enriched myeloid cells exhibits the highest immune-inhibitory signatures in big data.

a–c. UMAP projection of 455,959 immune cells in 131 CRC patients from syn26844071, GSE188711 and GSE178341, colored by cell type (a), tissue (b) and TNM stage (c). d. Immune-inhibitory scores of each cell type in tumor tissues. e. The significantly enriched signatures of TAM specific genes. Color depth represents -Log10 (p value). f-k. The expression of immune-inhibitory genes on macrophages (f) and neutrophils (g) from all the 131 CRC patients, as well as macrophages from stage I (h), stage II (i), stage III (j), and stage IV (k), between tumor and normal tissues (Wilcoxon test, two-sided).

Source data

Extended Data Fig. 3 The basic information of Sirpα knockout mice and its baseline immune profile.

a. Scheme of generation of Sirpa−/− mice. b, c. Sirpα protein in WT BMDMs and Sirpa−/− BMDMs were detected by Western blot (b) and FACS (c). d. Expression level of Sirpa, Sirpb1 and Sirpd in WT BMDMs (n = 3 samples) and Sirpa−/− BMDMs (n = 3 samples) were measured by RT–qPCR. e. Comparison of myeloid cell subsets in intestine between WT mice (n = 7 samples) and Sirpa−/− mice (n = 7 samples). f. Comparison of T cell subsets in intestine between WT mice (n = 7 samples) and Sirpa-/- mice (n = 6 samples). g. Comparison of fraction of NKT cells, NK cells, total myeloid cells, total T cells and B cells in all intestinal immune cells between WT mice (n = 7 samples) and Sirpa-/- mice (n = 7 or 6 samples). h-i. The gating strategies of myeloid cells (h) or lymphocytes (i). All samples were isolated from independent mice. Results (b, c) are from one representative of three independent experiments. Results (d-g) are from one independent experiment. Error bars show mean ± s.e.m. Unpaired Student’s t-test was used for d-g.

Source data

Extended Data Fig. 4 Sirpα deficiency suppresses tumor growth and Sirpα is associated with poor patient prognosis.

a. Survival analysis of WT mice (n = 12 mice) and Sirpa-/- mice (n = 13 mice) that bearing subcutaneous LLC tumor. b. Survival analysis of WT mice (n = 7 mice) and Sirpa-/- mice (n = 7 mice) that bearing subcutaneous Hepa1-6 tumor. c, e, g. The overall survival in patients with SIRPAhigh and SIPRAlow in colon cancer (GSE17536 and GSE17537, n = 232) (c), lung squamous carcinoma (TCGA-LUSC, n = 493) (e) and liver cancer (TCGA-LIHC, n = 365) (g). d, f, h. The disease-free survival in patients with SIRPAhigh and SIPRAlow in colon cancer (GSE17536 and GSE17537, n = 187) (d), lung squamous carcinoma (TCGA-LUSC, n = 476) (f) or liver cancer (TCGA-LIHC, n = 359) (h). i-j. Change of body weight (i) and disease activity index (j) for colitis of WT mice (n = 7 mice) and Sirpa-/- mice (n = 7 mice) during DSS induction. Disease activity index was calculated by summing the scores for body weight loss. k. The expression level of CD47 in MC38-Vector and MC38-CD47-/- cells in basal and inflammatory conditions (n = 3 independent experiments). l. The growths of MC38 tumors in WT mice without treatment (Ctrl; n = 6 mice), WT mice treated with anti-CD47 (n = 6 mice), WT mice treated with Sirpa-ED-FC protein (n = 6 mice) and Sirpa-/- mice without treatment (n = 9 mice). Data are pooled from 2 independent experiments (a), 3 independent experiments (k) or from one representative of five independent experiments(b), and from one independent experiment (i, j, l). P values (a-h) were performed with the log-rank test. P values (i, j, l) were used unpaired Student’s two-tailed t-test, and P values (k) were used ratio paired Student’s two-tailed t-test. Error bars show mean ± s.e.m.

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Extended Data Fig. 5 Sirpα deficiency reshapes cell constitution and immune function of TIICs.

a. t-SNE visualization of all immune cells colored by origins of WT mice and Sirpa-/- mice b. Average expression of top five differentially expressed genes in each cell type of all immune cells. c. Cell proportion of each cell type in AOM/DSS-induced tumor in WT mice and Sirpa-/- mice calculated by scDC. The p value were calculated by bootstrap resampling through GLM model in scDC. d. Percentages of immune cells in AOM/DSS-induced spontaneous colorectal tumors in WT mice (n = 9-12 samples) or Sirpa-/- mice (n = 6-9 samples) analyzed by flow cytometry. e. The number of differentially expressed genes of each cell type between AOM/DSS-induced CRC of WT mice and its Sirpa-/- mice counterpart. f. Comparisons of antigen presentation score, phagocytosis score and inflammatory response score of mMDSC between AOM/DSS-induced CRC of Sirpa-/- mice and WT mice using QuSAGE. X-axis represents log2 (fold change) of the gene set score. g-i. The significantly enriched signatures of ILCs (g), B cells (h) and plasma cells (i) in AOM/DSS-induced CRC of Sirpa-/- mice compared with their counterpart in WT mice (Wilcoxon test, two-sided). All samples were isolated from independent mice. Data (d) are mean ± s.e.m pooled from 4 independent experiments, and performed with unpaired Student’s two-tailed t-test.

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Extended Data Fig. 6 Sirpα deficiency reprogramed macrophage subsets and lineage in tumor.

a. Expression level of the top 3 subset-specific genes of each subset in all myeloid cell subsets. b. The top significantly enriched signatures of cluster mMDSC1-2 and TAM1-4. c. Smoothed gene expression trends of the top 100 genes whose expression values correlate best with lineage 1 (to TAM3) fate probabilities, sorted according to peak in pseudotime. Not all gene names are shown. d. Smoothed expression trends in pseudotime for the lineage 1 (to TAM3) related genes Dnase1l3, Cd81, S100a10 and Tmsb10 (95% CI). e. Expression of upregulated genes (top) and down-regulated genes (bottom) in TAM3 compared to TAM2. All genes show significantly statistic differences between TAM3 and TAM2 subsets (Wilcoxon test, two-sided, p < 0.05).

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Extended Data Fig. 7 Sirpα deficiency reprogramed gMDSCs subsets and lineage in tumor.

a. The significantly enriched signatures of cluster gMDSC1-gMDSC8. b, c. Smoothed gene expression trends of the top 100 genes whose expression values correlate best with the fate probabilities of lineage 1 (to gMDSC7) (b) or lineage 2 (to gMDSC8) (c), sorted according to peak in pseudotime. Not all gene names are shown. d. Smoothed expression trends in pseudotime for the lineage 1 (to gMDSC7) and lineage 2 (to gMDSC8) related genes Ifit3, Rsad2, Ftl1 and Cstb (95% CI). e. Expression of upregulated genes (top) and down-regulated genes (bottom) in gMDSC6/gMDSC7 compared to gMDSC5. All genes show significantly statistic differences between gMDSC6/gMDSC7 and gMDSC5 subsets (Wilcoxon test, two-sided, p < 0.05).

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Extended Data Fig. 8 Sirpα-/- gMDSCs have weak effect in antitumor immunity and TAMs.

a. t-SNE visualization of Sirpa in TIICs from WT mice, colored by expression level. b. Expression of Sirpa in each immune cell type in mouse tumor microenvironment. Middle white dot and line represent median and quartile values. c. The growth of subcutaneous MC38 tumor in WT (n = 9 mice), Sirpa-/- (n = 6 mice), WT + αLy6G (n = 7 mice) and Sirpa-/- + αLy6G group (n = 5 mice). d. The growth of subcutaneous MC38 tumor in WT (n = 5 mice), Sirpa-/- (n = 5 mice), WT + WT-MDSCs (n = 6 mice) and WT + Sirpa-/–MDSCs group (n = 7 mice). e. Enrichment signatures of differentially expressed genes of TAMs between Sirpa-/- mice and WT mice in MC38 model (bulk RNA-seq data) (Wilcoxon test, two-sided). f. Heat map of differentially expressed genes of TAMs between Sirpa-/- and WT mice in MC38 model, in which mainly showed the genes related to chemokine, inflammatory response, phagocytosis and antigen presentation (bulk RNA-seq data). g. Enrichment signatures of Sirpa-/- mice highly expressed genes in TAMs shared between MC38 model (bulk RNA-seq data) and AOM/DSS model (scRNA-seq data) (Wilcoxon test, two-sided). Result (c) is one representative of three independent experiments. Result (d) is from one independent experiment. Error bars show mean ± s.e.m. Unpaired Student’s two-tailed t-test was used for c, d.

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Extended Data Fig. 9 Sirpa-/- myeloid cells enhance the activation of T lymphocytes.

a, b. Expression of differentially expressed genes in CD8+ T cell (a) or CD4+ T cell (b) between WT mice and Sirpa-/- mice in AOM/DSS-induced CRC (Wilcoxon test, two-sided). c-d. Tumor loads (c) and tumor numbers (d) were measured in AOM/DSS-induced WT mice and Sirpa-/- mice treated with CD4+ or CD8+ antibody, WT (n = 9 mice), Sirpa-/- (n = 6 mice), WT + αCD4 (n = 5 mice), Sirpa-/- + αCD4 (n = 3 mice), WT + αCD8 (n = 3 mice), Sirpa-/- + αCD8 (n = 4 mice). e. The L–R pairs in cell–cell contact signaling from TAM, mMDSC, cDC1 and cDC2 (sender) to other cell types (receiver) in AOM/DSS-treated WT mice and Sirpa-/- mice calculated by CellChat. Line width indicates signal strength and line color is the same as signaling sender. f. The dominant sender and receiver cell types of cell–cell contact signaling network calculated by CellChat. Count represents the number of L–R pairs. g. The CD8+ T cells proliferation suppressed by WT (n = 3 samples) or Sirpa-/- (n = 3 samples) gMDSCs at different ratios. All samples were isolated from independent mice Result (c, d, g) are from one independent experiment. Error bars show mean ± s.e.m. Unpaired Student’s two-tailed t-test was used for c, d, g.

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Extended Data Fig. 10 Sirpa-/- myeloid cells enhance the recruitment of T lymphocytes.

a. The L–R pairs in secreting signaling from mMDSC, TAM and gMDSC (sender) to other cell types (receiver) in AOM/DSS-treated WT mice and Sirpa-/- mice calculated by CellChat. Line width indicates signal strength and line color is the same as signaling sender. b. The dominant sender and receiver cell types of chemokine signaling pathways (CCL and CXCL pathways) calculated by CellChat. Count represents the number of L–R pairs. c. The gene expression levels of various chemokines in TAMs of MC38 tumors from WT mice (n = 4 samples) or Sirpa-/- mice (n = 5 samples). d. Boxplot showing the SIRPA expression on macrophages in recurrent GBM patients pre- and post-neoadjuvant PD-1 blockade treatment. All samples were isolated from independent mice.The two-sided unpaired t-test was used to calculate P value. Result (c) is from one independent experiment. Error bars show mean ± s.e.m. Unpaired Student’s two-tailed t-test was used for c.

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Supplementary information

Reporting Summary

Supplementary Table

Supplementary Tables 1–4.

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Unprocessed H&E staining of colorectal tissue from WT mice (with tumor) and Sirpa−/− mice (without tumor).

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Unprocessed western blot for detection of SIRPA in WT mice and Sirpa−/− mice.

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Huang, C., Wang, X., Wang, Y. et al. Sirpα on tumor-associated myeloid cells restrains antitumor immunity in colorectal cancer independent of its interaction with CD47. Nat Cancer 5, 500–516 (2024). https://doi.org/10.1038/s43018-023-00691-z

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