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CD160 dictates anti-PD-1 immunotherapy resistance by regulating CD8+ T cell exhaustion in colorectal cancer

Nature Cell Biology volume 27, pages 1555–1571 (2025)Cite this article

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Abstract

The colon exhibits higher propensity for tumour development than ileum. However, the role of immune microenvironment differences in driving this disparity remains unclear. Here, by comparing paired ileum and colon samples from patients with colorectal cancer (CRC) and healthy donors, we identified ileum-enriched CD160+CD8+ T cells with previously unrecognized characteristics, including resistance to terminal exhaustion and strong clonal expansion. The transfer of CD160+CD8+ T cells significantly inhibits tumour growth in microsatellite instability-high and inflammation-induced CRC models. Cd160 knockout accelerates tumour growth, which is mitigated by transferring CD160+CD8+ T cells. Notably, in microsatellite instability-high and anti-PD-1-resistant CRC models, CD160+CD8+ T cells improve anti-PD-1 efficacy and overcome its resistance by increasing tumour-infiltrating progenitor-exhausted T cells, nearly eradicating tumours. Mechanistically, we uncover a CD160–PI3K (p85α) interaction that promotes FcεR1γ and 4-1BB expression via the AKT–NF-κB pathway, thereby enhancing CD8+ T cell cytotoxicity. Our study reveals CD160 as a crucial regulator of CD8+ T cell function and proposes an innovative immunotherapy strategy of transferring CD160+CD8+ T cells to overcome anti-PD-1 resistance.

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Fig. 1: Single-cell immune landscape in matched ileum, colon and colorectal tumours.
Fig. 2: Immune characteristics of T cell subsets across ileum and colon tissues both in healthy donors and patients with CRC.
Fig. 3: Lineage tracing of clonal state transition of CD8+ T cells.
Fig. 4: CD160+CD8+ T cells exhibit strong cytotoxicity and resistance to exhaustion with adoptive transfer inhibiting CRC progression.
Fig. 5: CD160 deficiency accelerates tumour progression and promotes terminal exhaustion of tumour-infiltrating CD8+ T cells.
Fig. 6: Tumour-infiltrating CD160+CD8+ T cells confer sensitivity to immunotherapy and overcome resistance to anti-PD-1 in MSI-H CRC.
Fig. 7: CD160 enhances CD8⁺ T cell cytotoxicity by upregulating FcεR1γ and 4-1BB expression.
Fig. 8: CD160 interacts with PI3K p85α to promote NF-κB nuclear translocation.

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Data availability

All data needed to evaluate the conclusions in this study are present in the Article or its Supplementary Information. The raw sequence data reported in this Article have been deposited in the Genome Sequence Archive66 in the National Genomics Data Center67 via accession number HRA006401 for human scRNA/BCR/TCR-seq data, HRA006350 for human bulk RNA-seq data and CRA019585 for mouse bulk RNA-seq data. Processed scRNA/BCR/TCR-seq data and processed human bulk RNA-seq data are available are available via the Mendeley Data Repository at https://doi.org/10.17632/6czch25jyb.1 (ref. 68) and https://doi.org/10.17632/hb9jjk2gbz.1 (ref. 69). Processed mouse bulk RNA-seq data are available via the Mendeley Data repository at https://doi.org/10.17632/7tgb8gnb8f.1 (ref. 70). The processed public datasets were collected from different databases, including GEO (https://www.ncbi.nlm.nih.gov/geo/, GSE235919, GSE39582, GSE21510 and GSE205506), the GDC data portal (https://portal.gdc.cancer.gov/, TCGA-COAD and TCGA-READ) and the GTEx portal (https://www.gtexportal.org/). All other data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Codes used for analysis and cell annotation are available via GitHub at https://github.com/HaoLabHMU/Colon-Adenocarcinoma.

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Acknowledgements

This study was supported by The Tou-Yan Innovation Team Program of the Heilongjiang Province (grant 2019-15 to X.Z.). We thank H. Liang and Y. Luo of The University of Texas MD Anderson Cancer Center for their comments and suggestions on the paper. We thank P. Ding of Sun Yat-sen University Cancer Center for their assistance of partial biological samples in the study.

Author information

Author notes

  1. Yingjian Liang & Sheng Tai

    Present address: Department of Hepatobiliary and Pancreatic Surgery, Harbin Medical University Cancer Hospital, Harbin, China

  2. These authors contributed equally: Tongsen Zheng, Chujie Ding, Shihui Lai, Yang Gao.

Authors and Affiliations

  1. Key Laboratory of Molecular Oncology of Heilongjiang Province, Harbin, China

    Tongsen Zheng, Chujie Ding, Shihui Lai, Yang Gao, Caiqi Liu, Jiaqi Shi, Mingwei Li, Yan Zhang & Feng Geng

  2. Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China

    Tongsen Zheng, Shihui Lai, Yang Gao, Caiqi Liu, Jiaqi Shi, Mingwei Li & Yan Zhang

  3. School of Basic Medical Sciences, Harbin Medical University, Harbin, China

    Cheng Lyu & Dapeng Hao

  4. Department of Pathology, Harbin Medical University, Harbin, China

    Xiaobo Li

  5. Department of Pathology, Harbin Medical University Cancer Hospital, Harbin, China

    Hongxue Meng

  6. Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, China

    Mingqi Li, Li Li & Peng Han

  7. Departments of General Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, China

    Yingjian Liang

  8. Department of Hepatic Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China

    Sheng Tai

  9. College of Bioinformatics Science and Technology, NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China

    Liang Cheng

  10. Research Center for Pharmacoinformatics (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), College of Pharmacy, Harbin Medical University, Harbin, China

    Bin Sun & Te Liu

  11. NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China

    Xue Zhang

  12. McKusick-Zhang Center for Genetic Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Xue Zhang

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Contributions

Conceptualization by T.Z., D.H. and X.Z. Methodology by D.H., C. Lyu., C.D. and X.L. Validation by C.D., S.L. and Y.G. Formal analysis by D.H., C. Lyu., B.S. and T.L. Data curation by L.C. Drafting and editing by T.Z., D.H., C.D., S.L. Y.G. and F.G. Investigation by C. Liu., J.S., Mingwei Li and Y.Z. Visualization by D.H., C. Lyu., S.L. and Y.G. Funding acquisition by X.Z. and T.Z. Resources from H.M., Mingqi Li, Y.L., S.T., L.L. and P.H. Supervision by T.Z., D.H. and X.Z. All authors revised and reviewed the paper and approved the final version.

Corresponding authors

Correspondence to Tongsen Zheng, Dapeng Hao or Xue Zhang.

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Nature Cell Biology thanks Ping-Chih Ho, Dirk Jäger 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 Analysis of immune cells of CRC patients and healthy donors.

a, UMAP plots showing 208,004 B lineage cells. Cells are color labeled with their inferred cell types/states based on transcriptional profiles (left) and antibody isotypes using scBCR-seq data (right). b, Bubble plots showing the expression of selected marker genes for B cells subtypes as defined in (a). Dot size indicates frequencies of expressing cells, colored according to average expression levels. c, UMAP plots showing subtypes of myeloid lineage cells. d, Bubble plot showing the expression of select marker genes for myeloid cells subtypes as defined in (c). Dot size indicates frequencies of expressing cells, colored according to average expression levels. e, The same UMAP as in Fig. 1b but with cells color coded by their BCR (left) and TCR (right) expression. f, UMAP plot showing all the T cells colored by 6 subtypes. g, Bubble plots showing average expression levels and proportions of select marker genes for different tissues (upper) and T cells subtypes as defined in (f) (bottom). Dot size indicates frequencies of expressing cells, colored according to normalized average expression levels. h, Heatmap displaying the fraction of T cells subtypes across multiple tissues. i, UMAP plots displaying the expression of CD160 for T cells from ileum (upper), colon (middle) and tumor (bottom).

Extended Data Fig. 2 Differences of the cellular composition, distribution and transcriptional states across different tissues.

a, The same UMAP as in Extended Data Fig. 1a, but with cells colored by TGFB1 expression. b–d, Bar graph displaying the composition of B cell subtypes (b) and antibody isotypes (c) across different tissues, and the landscape of antibody isotypes compositions across B cell subtypes (d) in CRC patients and healthy donors. e, Box plots showing the paired comparisons of cell proportions of monocyte and mast cells among matched tissues from the same CRC patients (n = 10). P values are determined by paired two-sided Mann-Whitney test. f, Patients from the TCGA CRC cohort that are ranked by signature score constructed using specific marker genes of mast cells and monocytes. Signature score is defined as the T-test score comparing normalized expression of mast cell markers versus monocyte markers. Samples are divided into 3 approximately equal groups according to the score (upper). The heatmap illustrating the expression of marker genes across tumor samples (middle). Estimates are made for the dependence of all-time risk of death on the signature score (bottom). P value is determined by the cox proportional hazards (CoxPH) model, and the dotted curves represent the 95% confidence interval (CI) of the log hazard ratio. Hi, high; lo, low. g, Unsupervised hierarchical clustering dendrogram of myeloid cells, NK cells and B cells derived from the average transcriptome profiles across tissues in CRC patients and healthy donors. h, Volcano plots illustrating the differentially expressed genes in IELs between colon and ileum of CRC patients (left) and healthy donors (right). Genes with significant differential expression and consistent tendency in CRC patients and healthy donors are indicated in green or purple dots and bold labels. i, Heatmap displaying expression of top DEGs of ileum vs. colon across ileum, colon and tumor in CRC patients and healthy donors. Box plots: center line, median; box limits, first and third quartile; whiskers, 1.5× interquartile range.

Extended Data Fig. 3 Bulk RNA-seq analysis of transcriptional differences across different intestinal tissues and single-cell immune profiling of CD4+ and CD8+ T cells.

a, Dendrogram showing unsupervised hierarchical clustering of tissue samples from CRC patients and healthy donors that were run on the expression of immune-related genes. b, Kaplan Meier curves displaying differences of DFS between the two clusters in TCGA-COAD cohort. c, Scatter plots showing immune genes expression fold change (log2) between C1 and C2 (y-axis) against the corresponding values of ileum and colon (x-axis). Dots in red circle represent top immune genes with significant expression change on both axes. Pathways enrichment of the selected immune genes and the most enriched pathways are labeled on the top. d, Box plots showing normalized T cell activation pathway enrichment score in different clusters across CRC tumors and normal tissues, ordered by decreasing signature score. The number of samples in each group is listed below the boxes. e, Scatter plot showing the correlation of differentially expressed immune related genes between ileum and colon in CRC patients (y-axis) against the corresponding values in healthy donors (x-axis). f, The same UMAP as in Fig. 2a but with cells colored by TCR productivity and gene expression of TYROBP, LEF1, CD160. g, Bubble plot showing the expression of indicated genes for CD8+ T cells and IEL-T subtypes as defined in Fig. 2a. Dot size indicates frequencies of expressing cells, colored according to average expression levels. h, UMAP plot showing subtypes of CD4+ T cells. i, Bubble plot showing the expression of select marker genes for CD4+ T cells subtype as defined in (h). Dot size indicates frequencies of expressing cells, colored according to average expression levels. j, UMAP plot showing subtypes of CD8+ T cells from PBMC and LN. k, Bubble plot showing the expression of select marker genes for CD8+ T cells subtype from PBMC and LN. Dot size indicates frequencies of expressing cells, colored according to average expression levels. l, Box plots showing the paired comparisons of cell proportions of TReg cells, TNaive cells, TCM cells, TFH cells, TCTL cells and TEM cells in CD4+ T cells among matched tissues from the same CRC patients (n = 10) and healthy donors (n = 3). m, Box plots showing the paired comparisons of cell proportions of GZMK+ eff. cells, TMem cells, TNaive cells and MAIT cells in CD8+ T cells among matched tissues from the same CRC patients (n = 10) and healthy donors (n = 3). n, Box plots showing neoantigen reactivity for CD4+ (left) and CD8+ (right) cells across the indicated cell subtypes. o, Box plots showing CD4+ neoantigen reactivity of TReg and TFH cells across the indicated tissue types in CRC patients and healthy donors. P values are determined by paired two-sided Mann-Whitney test (d, l, m and o). Box plots: center line, median; box limits, first and third quartile; whiskers, 1.5× interquartile range.

Extended Data Fig. 4 Relationship between transition of T cell exhaustion state and CD160, and distribution of CD160+CD8+ T cells in different intestinal tissues.

a, Box plots showing normalized IEL signature in different clusters, ordered by decreasing values. The number of samples in each group is listed below the boxes. b, Monocle trajectory plots of TCRαβ+ IEL-T cells. Cells are colored by the Exh_Tex score (left) and MKI67 expression (right). c, Monocle trajectory reconstruction of TCRαβ+ IEL-T cells. Regression lines are fitted against pseudotime for EOMES, IL7R, TOX, REL expression of different tissue types. d, Box plots demonstrating the expression of CD160 in ileum, colon and tumor tissues from GTEx, TCGA-CRC, GSE39582, and GSE21510 datasets. e, f, Representative flow cytometry plots (e) and frequencies (f) of CD160 in CD8+ T cells among different intestinal tissues from C57BL/6 mice (n = 3 mice). g, Experimental scheme of AOM/DSS-induced CRC mouse model. h, Representative small intestine and colon images of AOM/DSS-induced CRC mouse (n = 6 mice). i, j, Representative flow cytometry plots (i) and frequencies (j) of CD160 in CD8+ T cells among ileum, colon and tumor from AOM/DSS-induced CRC mice (n = 4 mice). k, l, Representative immunofluorescence staining (k) and quantification (l) of ileum, colon and tumor from AOM/DSS induced CRC mouse. Staining for CD8 (green), CD160 (red) and DAPI (blue). Red triangles highlight CD160+CD8+ T cells. Scale bars: 50 μm (n = 4 mice). m, Box plots showing the Exh_Pex score (left) and Exh_Tex score (right) of tumoral TCRαβ+ IELs per indicated regional pattern. n, Box plots showing the Exh_Pex score (left) and Exh_Tex score (right) across CD160+ and CD160− cells in tumoral CD8+ T cells from GSE205506 dataset. o, Box plots showing the Exh_Pex score (left) and Exh_Tex score (right) of tumoral CD8+ T cells per indicated regional pattern. p, Bar plots showing the fraction of tumoral CD8+ T cells that are clonally linked with cells from other indicated tissues. Cells are divided by the cutoff 0.45 of Exh_Tex score, and only cells from clones containing at least 2 tumoral CD8+ T cells are included. P values are determined by paired two-sided Mann-Whitney test (a, d, m, n and o). Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (f, j, and l). Box plots: center line, median; box limits, first and third quartile; whiskers, 1.5× interquartile range.

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Extended Data Fig. 5 Conserved transcriptomes and progenitor exhausted phenotype of CD160⁺CD8⁺ T cells across tissues.

a, Clustering and correlation of CD160⁺CD8⁺ T cells across samples indicate higher variation across individuals than across tissues. Clustering is based on Euclidean distances between the average transcriptome of samples. b-d, Volcano plots illustrating DEGs in CD160+CD8+ T cells (b) and CD160−CD8+ T cells (c) between healthy donors and CRC patients, and CD160+CD8+ T cells (d) between tumor and normal tissues from CRC patients. Contamination signals are highlighted due to subtle transcriptomic differences in (d), as shown by the top DEGs such as JCHAIN and IGHG1. e, GSEA of GO biological processes in CD160+ and CD160− CD8+ T cells between healthy donors and CRC patients that indicates the enrichment of metabolism- and immune-related pathways, respectively. f, g, Representative flow cytometry plots (f) and frequencies (g) of TCF1+PD-1+ in CD160+CD8+ and CD160−CD8+ T cells from indicated tissues of MC38 tumor-bearing mice (n = 6 mice). Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (g).

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Extended Data Fig. 6 Adoptive transfer of CD160+CD8+ T cells inhibit the growth of MC38 subcutaneous tumor.

a, Schematic representation of co-transfer of CD160+CD8+ T cells (GFP+) and CD160−CD8+ T cells (CD45.1+) into MC38 tumor-bearing mice. b, Representative flow cytometry identification plots of splenocytes from CD45.1 and GFP mice. c, d, Representative flow cytometry plots (c) and frequencies (d) of GFP+CD45.1− and GFP−CD45.1+ in tumors 36 hours after transfer (n = 6 mice). e, f, Representative flow cytometry plots (e) and frequencies (f) of CD45.1+Tetramer+ cells in tumors at indicated timepoints after adoptive transfer CD160+CD8+ T cells from CD45.1 mouse spleens into established MC38-OVA tumors (n = 5 mice). g, h, Representative flow cytometry plots (g) and frequencies (h) of CD160 in CD8+ T cells isolated from tumor tissues, as shown in Fig. 4m (n = 5 mice). i, Representative flow cytometry plots of GzmB in CD8+ T cells isolated from tumor tissues, as shown in Fig. 4m. j, Experimental scheme for adoptive T cell transfer in established MC38 tumors. Splenic CD160+CD8+ and CD160−CD8+ T cells from normal syngeneic C57BL/6 mice were transferred via tail vein injection at the indicated time points. k, l, Representative tumor images (k) and tumor volume (l) in MC38 tumor mouse model, as shown in (j) (n = 5 mice). m, n, Representative flow cytometry plots (m) and frequencies (n) of CD160 in CD8+ T cells isolated from tumor tissues, as shown in (j) (n = 5 mice). o, p, Representative flow cytometry plots (o) and frequencies (p) of GzmB in CD8+ T cells isolated from tumor tissues in MC38 tumor-bearing mice, as shown in (j) (n = 5 mice). Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (d, h, l, n and p).

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Extended Data Fig. 7 Effects of CD160 expression levels on CD8+ T cell anti-tumor function in MSS CRC model and validation of CD160 knockout efficiency of Cd160−/− mice.

a, Experimental scheme for adoptive T cell transfer while inoculating the CT26 cell line into Balb/c mice. Splenic CD160+CD8+ and CD160−CD8+ T cells from normal syngeneic Balb/c mice were transferred via tail vein injection at the indicated time points. b, c, Representative tumor images (b) and tumor volume (c) in CT26 tumor-bearing mice, as shown in (a) (n = 5 mice). d, Frequencies of CD160 in CD8+ T cells isolated from tumor tissues in CT26 tumor-bearing mice, as shown in (a) (n = 5 mice). e, Frequencies of GzmB in CD8+ T cells isolated from tumor tissues in CT26 tumor-bearing mice, as shown in (a) (n = 5 mice). f, Experimental scheme for adoptive T cell transfer in established CT26 tumors. Splenic CD160+CD8+ and CD160−CD8+ T cells from normal syngeneic Balb/c mice were transferred via tail vein injection at the indicated time points. g, h, Representative tumor images (g) and tumor volume (h) in CT26 tumor-bearing mice, as shown in (f) (n = 4 mice). i, Frequencies of CD160 in CD8+ T cells isolated from tumor tissues in CT26 tumor-bearing mice, as shown in (f) (n = 4 mice). j, Frequencies of GzmB in CD8+ T cells isolated from tumor tissues in CT26 tumor-bearing mice, as shown in (f) (n = 4 mice). k, Scheme of generation of Cd160−/− mice. l–n, Representative flow cytometry plots and frequencies of CD160 in CD8+ T cells isolated from spleen (l), small intestine (m) and colon (n) in WT and Cd160−/− mice. o, p, Representative flow cytometry plots (o) and frequencies (p) of CD160 in CD8+ T cells isolated from tumor tissues in WT and Cd160−/− mice bearing MC38 tumor, as shown in Fig. 5e (n = 6 mice). Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (c, d, e, h, i, j, l, m, n and p).

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Extended Data Fig. 8 Adoptive transfer of CD160+CD8+ T cells combined with anti-PD-1 therapy inhibits CRC progression and overcomes anti-PD-1 resistance.

a, b, Representative flow cytometry plots (a) and frequencies (b) of CD160 in CD8+ T cells isolated from tumor tissues in MC38 tumor-bearing mice, as shown in Fig. 6a (n = 6 mice). c, Representative flow cytometry plots of GzmB in CD8+ T cells isolated from tumor tissues in MC38 tumor-bearing mice, as shown in Fig. 6a. d, e, Representative flow cytometry plots (d) and frequencies (e) of TCF1−PD-1+ in CD8+ T cells isolated from tumor tissues in MC38 tumor-bearing mice, as shown in Fig. 6a (n = 6 mice). f, Experimental scheme for adoptive T cell transfer combined with anti-PD-1 therapy in AOM/DSS-induced CRC mice. Splenic CD160−/+CD8+ T cells from normal syngeneic C57BL/6 mice were adoptively transferred via tail vein injection, while AOM/DSS-induced CRC mice were administrated anti-PD-1 at the indicated time points. g, Representative images of colon tumors in AOM/DSS-induced CRC mice, as shown in (f) (n = 8–13 mice). h-j Total tumor number (h) and tumor size (i) in colon tissues and survival curve (j) were calculated under the indicated treatments, as shown in (f) (n = 8–13 mice). k, Box plots showing CD8A expression across different response groups of CRC patients received first line immunotherapy (CR n = 6; PR n = 17; SD n = 11). P values are determined by paired two-sided Mann-Whitney test. l, Kaplan–Meier curves displaying differences in PFS between CRC patients grouped by the high or low expression of CD8A. P value is determined by long-rank test. m, Representative flow cytometry plots of GzmB in CD8+ T cells isolated from tumor tissues in anti-PD-1 resistant model mice, as shown in Fig. 6m (n = 6 mice). n, Representative flow cytometry plots of TCF1+PD-1+ in CD8+ T cells isolated from tumor tissues in anti-PD-1 resistant model mice, as shown in Fig. 6m (n = 6 mice). Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (b, e, h, i and j). Box plots: center line, median; box limits, first and third quartile; whiskers, 1.5× interquartile range.

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Extended Data Fig. 9 CD160 regulates Fcer1g and Tnfrsf9 expression to affect the cytotoxic function of CD8+ T cells.

a, Relative mRNA expression of Fcer1g in CD160−/+CD8+ T cells with or without anti-CD3/CD28 stimulation for 24 hours (n = 3 biological replicates). b, Relative mRNA expression of Cd160 in CD160−CD8+ T cells overexpressing Cd160 (n = 3 biological replicates). c, Relative mRNA expression of Fcer1g in CD160+CD8+ T cells after knocking down Fcer1g (n = 3 biological replicates). d, Relative mRNA expression of Fcer1g in CD160−CD8+ T cells overexpressing Fcer1g (n = 3 biological replicates). e, Relative mRNA expression of Tnfrsf9 in CD160−/+CD8+ T cells with or without anti-CD3/CD28 stimulation for 24 hours (n = 3 biological replicates). f, Relative mRNA expression of Tnfrsf9 in CD160+CD8+ T cells after knocking down Tnfrsf9 (n = 3 biological replicates). g, Relative mRNA expression of Tnfrsf9 in CD160−CD8+ T cells overexpressing Tnfrsf9 (n = 3 biological replicates). h–j, Representative flow cytometry plots (h) and frequencies of FcεR1γ+ (i) and 4-1BB+ (j) cells in CD160+ versus CD160− CD8+ T cells isolated from tumor tissues of CRC patients (n = 6 samples). k–m, Representative flow cytometry plots (k) and frequencies of FcεR1γ+ (l) and 4-1BB+ (m) cells in tumor-infiltrating CD160+ versus CD160− CD8+ T cells from MC38 tumor-bearing mice (n = 6 mice). n, Representative images of NF-κB p65 and NF-κB p52 protein expression in CD160−/+CD8+ T cells with or without anti-CD3/CD28 stimulation for 24 hours analysed by Western blot. o, Binding of HA-tagged CD160 to the Flag-tagged PI3K p85α and p110δ in HEK293T cells. p, PLA signals between PI3K and CD160 in CD160−CD8+ T cells. Scale bars: 10 μm. q, Predicted binding models, stability, and binding free energy of CD160 interactions with p110α, p110β, p110δ. r, Knockdown efficiency of p85α and p110δ siRNA was verified in CD160+CD8+ T cells. Data are shown as the mean ± s.e.m, P values were determined by unpaired two-tailed Student’s t-test (a, b, c, d, e, f, g, i, j, l and m).

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Extended Data Fig. 10 Representative gating strategies for T lymphocytes.

a, FACS gating strategy of CD160+ in CD8+ T cells isolated from intestine and spleen. Related to Fig. 2k; Extended Data Fig. 4e, i; Extended Data Fig. 7l, m, n. b, FACS gating strategy for in vitro exhaustion assay. Related to Fig. 4d. c, FACS gating strategy for co-culture system. Related to Fig. 4g. d, FACS gating strategy of CD160+ and GzmB+ cells in tumor-infiltrating CD8+ T cells. Related to Fig. 5h; Extended Data Fig. 6g, i, m, o; Extended Data Fig. 7o; Extended Data Fig. 8a, c, m; Extended Data Fig. 9h, k. e, FACS gating strategy of CD160+CD8+ T cells (GFP+CD45.1−) and CD160−CD8+ T cells (GFP−CD45.1+) int tumor. Related to Extended Data Fig. 6c. f, FACS gating strategy of CD45.1+Tetramer+ cells in tumor. Related to Extended Data Fig. 6e. g, FACS gating strategy of progenitor exhausted CD8+ T cells in tumor. Related to Fig. 5l; Extended Data Fig. 8d, n. h, FACS gating strategy of FcεR1γ+ in CD160−CD8+ T cells or CD160+CD8+ T cells. Related to Fig. 7e. i, FACS gating strategy of 4-1BB+ in CD160−CD8+ T cells or CD160+CD8+ T cells. Related to Fig. 7i. j, FACS gating strategy of GzmB+ in CD160−CD8+ T cells or CD160+CD8+ T cells. Related to Fig. 4a; Fig. 7f, g, j, k. k, FACS sorting strategy of CD160+CD8+ T cells from spleen and purity validation.

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Supplementary Table 1

Metadata of the CRC patients and healthy donors.

Supplementary Table 2

Transcriptional signatures used in the study.

Supplementary Table 3

Predictive genes of favourable prognosis in TCGA CRC samples used in the study.

Supplementary Table 4

Metadata of the CRC patients that received anti-PD-1 treatment.

Supplementary Table 5

Reagents or resources.

Supplementary Table 6

Primers for RT–qPCR.

Supplementary Table 7

shRNA or siRNA for transfection in this study.

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Zheng, T., Ding, C., Lai, S. et al. CD160 dictates anti-PD-1 immunotherapy resistance by regulating CD8+ T cell exhaustion in colorectal cancer. Nat Cell Biol 27, 1555–1571 (2025). https://doi.org/10.1038/s41556-025-01753-3

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