Multi-omics profiling identifies TNFRSF18 as a novel marker of exhausted CD8⁺ T cells and reveals tumour-immune dynamics in colorectal cancer

Abstract

Background: Colorectal cancer (CRC) ranks among the most prevalent malignant tumours of the digestive system globally and is associated with unfavourable survival outcomes. The exhaustion of CD8⁺ T cells serves a crucial role in facilitating tumour immune escape. Yet, the dynamic evolution of CD8⁺ T cell exhaustion and its impact on clinical prognosis across TNM (tumour-node-metastasis) stages in CRC remains incompletely characterized.

Methods: Tumour and adjacent tissues (20 samples total) from 6 CRC patients spanning diverse TNM stages were analyzed using integrated single-cell transcriptomic profiling (scRNA-seq), single-cell T cell receptor/B cell receptor sequencing (scVDJ-seq), and spatial transcriptomics. T cell exhaustion markers, immune clonality, gene expression profiles, and the spatial distribution of both tumour cells and immune cells were systematically profiled. Functional enrichment and intercellular communication analyses were conducted. Key findings were validated using immunofluorescence and public datasets.

Results: Our results illustrate how advancing TNM stages in CRC shape CD8⁺ T cell exhaustion through divergent TNFRSF18/CXCL13 dynamics and ribosomal stemness. TNFRSF18 expression was notably higher in T cells infiltrating tumour tissues relative to their counterparts in adjacent non-tumorous areas, with high-expressing CD8⁺ T cells exhibiting marked exhaustion features. During CRC progression, TNM-stage-driven remodelling of the tumour microenvironment (TME) induced progressive CD8⁺ T cell exhaustion marked by declining TNFRSF18 and rising CXCL13 expression in tumour-infiltrating T cells elevation of both markers in the tumour compared with adjacent tissues. Moreover, we show that tumour cells displayed elevated expression of stemness-associated ribosomal genes (RPS7, RPL8, RPL30), peaking at stage T4, which correlated with poor prognosis and immune escape.

Conclusions: This integrative multi-omics study uncovers CD8⁺ T cell exhaustion dynamics and ribosomal stemness-mediated immune evasion across CRC progression. CXCL13, TNFRSF18, and ribosomal proteins (RPS7/RPL8/RPL30) are identified as novel biomarkers with direct prognostic value and therapeutic relevance, providing therapeutic targets for precision immunotherapy in CRC.

Keywords

colorectal cancer / singlecell RNA sequencing / spatial transcriptomics / T cell exhaustion / TNFRSF18 (GITR) / TNM stage

References

[1] Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023; 72(2): 338-344.

[2] Zeineddine FA, Zeineddine MA, Yousef A, et al. Survival improvement for patients with metastatic colorectal cancer over twenty years. NPJ Precision Oncol. 2023; 7(1): 16.

[3] Galassi C, Chan TA, Vitale I, Galluzzi L. The hallmarks of cancer immune evasion. Cancer Cell. 2024; 42(11): 1825-1863.

[4] Peitzsch C, Tyutyunnykova A, Pantel K, Dubrovska A. Cancer stem cells: the root of tumor recurrence and metastases. Semin Cancer Biol. 2017; 44: 10-24.

[5] Chen K, Collins G, Wang H, Toh JWT. Pathological features and prognostication in colorectal cancer. Curr Oncol. 2021; 28(6): 5356-5383.

[6] Villani AC, Satija R, Reynolds G, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science. 2017; 356(6335).

[7] Zhao S, Zhang P, Niu S, et al. Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data. Funct Integr Genomics. 2024; 24(2): 72.

[8] Bredel D, Tihic E, Mouraud S, et al. Immune checkpoints are predominantly co-expressed by clonally expanded CD4+FoxP3+ intratumoral T-cells in primary human cancers. J Exp Clin Cancer Res. 2023; 42(1): 333.

[9] Sathe A, Ayala C, Bai X, et al. GITR and TIGIT immunotherapy provokes divergent multicellular responses in the tumor microenvironment of gastrointestinal cancers. Genome Med. 2023; 15(1): 100.

[10] Qi Y, Xia Y, Lin Z, et al. Tumor-infiltrating CD39(+)CD8(+) T cells determine poor prognosis and immune evasion in clear cell renal cell carcinoma patients. Cancer Immunol Immunother. 2020; 69(8): 1565-1576.

[11] Ebright RY, Lee S, Wittner BS, et al. Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis. Science. 2020; 367(6485): 1468-1473.

[12] Tian J, Zhang B, Rui K, Wang S. The role of GITR/GITRL interaction in autoimmune diseases. Front Immunol. 2020; 11: 588682.

[13] Nocentini G, Riccardi C. GITR: a modulator of immune response and inflammation. Adv Exp Med Biol. 2009; 647: 156-173.

[14] Li H, van der Leun AM, Yofe I, et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell. 2019; 176(4): 775-789.

[15] Nocentini G, Giunchi L, Ronchetti S, et al. A new member of the tumor necrosis factor/nerve growth factor receptor family inhibits T cell receptor-induced apoptosis. Proc Natl Acad Sci USA. 1997; 94(12): 6216-6221.

[16] So T, Ishii N. The TNF-TNFR family of co-signal molecules. Adv Exp Med Biol. 2019; 1189: 53-84.

[17] Genomics x. Cell Ranger 3.0.2. https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest

[18] Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13): 3573-3587. e29.

[19] Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019; 8(4): 281-291. e9.

[20] Laurens VDM, Hinton G. Visualizing Data using t-SNE. JLMR. 2008; 9(2605): 2579-2605.

[21] McInnes LH, John; Melville, James. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint arXiv:180203426. 2018;

[22] Korsunsky I, Fan J, Slowikowski K, et al. Fast, sensitive, and flexible integration of single cell data with Harmony. bioRxiv. 2018: 461954.

[23] Hu C, Li T, Xu Y, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023; 51(D1): D870-d876.

[24] Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4): 381-386.

[25] Qiu X, Hill A, Packer J, Lin D, Ma Y-A, Trapnell C. Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017; 14(3): 309-315.

[26] Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017; 14(10): 979-982.

[27] Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. The Innovation. 2021; 2(3).

[28] Xu S, Hu E, Cai Y, et al. Using clusterProfiler to characterize multiomics data. Nat Prot. 2024; 19(11): 3292-3320.

[29] Yu G. Thirteen years of clusterProfiler. The Innovat. 2024; 5(6).

[30] Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS: Journal Integr Biol. 2012; 16(5): 284-287.

[31] Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021; 12(1): 1088.

[32] Chang K, Creighton CJ, Davis C, et al. The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat Gen. 2013; 45(10): 1113-1120.

[33] Cable DM, Murray E, Zou LS, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022; 40(4): 517-526.

[34] Gao R, Bai S, Henderson YC, et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat Biotechnol. 2021; 39(5): 599-608.

[35] Gulati GS, Sikandar SS, Wesche DJ, et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science. 2020; 367(6476): 405-411.

[36] Wang Y, Feng H, Li X, et al. Dampening of ISGylation of RIG-I by ADAP regulates type I interferon response of macrophages to RNA virus infection. PLoS Pathog. 2024; 20(5): e1012230.

[37] Zhang P, Wang X, Yang X, Liu H. Molecular control of PDPNhi macrophage subset induction by ADAP as a host defense in sepsis. JCI Insight. 2025; 10(6).

[38] Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019; 20(2): 163-172.

[39] Zhang L, Yu X, Zheng L, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018; 564(7735): 268-272.

[40] Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017; 45(W1): W98-w102.

[41] Dai M, Pei X, Wang X-J. Accurate and fast cell marker gene identification with COSG. Brief Bioinform. 2022; 23(2).

[42] Liu B, Hu X, Feng K, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer. 2022; 3(1): 108-121.

[43] Alfei F, Kanev K, Hofmann M, et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature. 2019; 571(7764): 265-269.

[44] Khan O, Giles JR, McDonald S, et al. TOX transcriptionally and epigenetically programs CD8(+) T cell exhaustion. Nature. 2019; 571(7764): 211-218.

[45] Scott AC, Dündar F, Zumbo P, et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature. 2019; 571(7764): 270-274.

[46] Hosseini A, Gharibi T, Marofi F, Babaloo Z, Baradaran B. CTLA-4: from mechanism to autoimmune therapy. Int Immunopharmacol. 2020; 80: 106221.

[47] Amatore F, Gorvel L, Olive D. Role of inducible co-stimulator (ICOS) in cancer immunotherapy. Expert Opin Biol Ther. 2020; 20(2): 141-150.

[48] Webb GJ, Hirschfield GM, Lane PJ. OX40, OX40L and autoimmunity: a comprehensive review. Clin Rev Allergy Immunol. 2016; 50(3): 312-332.

[49] Xiao S, Lu L, Lin Z, et al. LAYN serves as a prognostic biomarker and downregulates tumor-infiltrating CD8(+) T cell function in hepatocellular carcinoma. J Hepatocell Carcinoma. 2024; 11: 1031-1048.

[50] Mishra A, Verma M. Epigenetic and genetic regulation of PDCD1 gene in cancer immunology. Methods Mol Biol. 2018; 1856: 247-254.

[51] Costea DE, Gammon L, Kitajima K, Harper L, Mackenzie IC. Epithelial stem cells and malignancy. J Anat. 2008; 213(1): 45-51.

[52] Ababneh O, Nishizaki D, Kato S, Kurzrock R. Tumor necrosis factor superfamily signaling: life and death in cancer. Cancer Metastasis Rev. 2024; 43(4): 1137-1163.

[53] Lu W, Kang Y. Epithelial-mesenchymal plasticity in cancer progression and metastasis. Dev Cell. 2019; 49(3): 361-374.

[54] Malta TM, Sokolov A, Gentles AJ, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018; 173(2): 338-354. e15.

[55] Xiao J, Yu X, Meng F, et al. Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer. Cell Death Dis. 2024; 15(5): 326.

[56] Wen Y, An Z, Qiao B, Zhang C, Zhang Z. RPS7 promotes cell migration through targeting epithelial-mesenchymal transition in prostate cancer. Urologic Oncol: Semin Orig Investig. 2019; 37(5): 297. e1-297.e7.

[57] Prosser A, Pushel I, Seidel C, et al. Stem cell model of novel rpl30 variant in diamond Blackfan anemia with downregulated GATA1-HSP70 in early erythroid progenitors. Blood. 2024; 144: 2710.

[58] Yang Z, Tian H, Chen X, et al. Single-cell sequencing reveals immune features of treatment response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma. Nat Commun. 2024; 15(1): 9097.

[59] Zhou Y, Zhao W, Zhu Y, et al. CXCL13 expression promotes CAR T cell antitumor activity and potentiates response to PD-1 blockade. Adv Sci (Weinh). 2025: e08095.

[60] Park S, Cha H, Kim HS, et al. Transcriptional upregulation of CXCL13 is correlated with a favorable response to immune checkpoint inhibitors in lung adenocarcinoma. Cancer Med. 2023; 12(6): 7639-7650.

[61] Yang M, Lu J, Zhang G, et al. CXCL13 shapes immunoactive tumor microenvironment and enhances the efficacy of PD-1 checkpoint blockade in high-grade serous ovarian cancer. J Immunother Cancer. 2021; 9(1).

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2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.