Abstract
T-cell receptor (TCR) repertoires are central to antitumor immunity, yet their dynamics in digestive system cancers remain poorly defined. We profiled TCR repertoires from 415 tumors in 145 patients with colorectal cancer (CRC, n = 96), gastric cancer (GC, n = 47), and hepatocellular carcinoma (LIHC, n = 2), integrating clinical and pathological features. Distinct repertoire architectures emerged: CRC was characterized by abundant TRB V–J combinations (e.g., TRBV10-2*00–TRBJ2-4*00), whereas GC showed higher abundance of TRG/TRD pairings (e.g., TRGV5P*00–TRGJP1*00, TRDV3*00–TRDJ1*00), reflecting tumor-specific immune surveillance. Conserved motifs (“CATWD,” “YKKLF”) across cancers indicate shared selective pressures, while antigen mapping revealed both common (KRAS, SF3B1, and BST2) and tumor-specific targets (MAGEA10, WT1 in CRC; PABPC1 in GC). In CRC, repertoire dynamics were tightly coupled to disease stage. Metastatic tumors (MT) displayed larger size, vascular invasion, and elevated serum markers, whereas primary tumors (PT) exhibited stronger immune infiltration with lymphocyte- and myeloid-driven responses. Tumor size was significantly and positively correlated with the number of TRD/TRG clonotypes shared between PT and MT. Shared clones were further classified into three categories, including stable, contracted, and expanded. Among these, expanded MT clones were dominated by the “NYGYTF” motif within the TRB chain (e.g., TRBV7-9*00–TRBJ1-2*00). The most abundant “NYGYTF”-containing clones recognized MLANA, a tumor-associated antigen linked to prognosis and therapeutic responsiveness, underscoring its potential role in CRC progression. Collectively, these findings delineate cancer- and stage-specific TCR repertoire alterations and antigen specificities, highlighting novel biomarkers and therapeutic targets to inform TCR-based diagnostics and personalized immunotherapies in CRC and GC.
Similar content being viewed by others
Data availability
The raw data generated in this study has been deposited at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1205408. Publicly available data utilized in this research were obtained from the TCGA-COAD transcriptomic expression dataset and associated clinical data. In addition, the GSE110224 dataset36 was obtained from the GEO database and comprises 17 normal tissue samples and 17 colorectal cancer (CRC) tumor samples.
Code availability
All the code used for the analysis can be accessed by the corresponding author on reasonable request.
References
Naimi, A. et al. Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons. Cell Commun. Signal. 20, 44 (2022).
Vafaei, S. et al. Combination therapy with immune checkpoint inhibitors (ICIs); a new frontier. Cancer Cell Int. 22, 1–27 (2022).
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).
Du, K. et al. Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma. Nat. Commun. 12, 1–16 (2021).
Hammerl, D. et al. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer. Nat. Commun. 12, 1–13 (2021).
Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).
Valkiers, S. et al. Recent advances in T-cell receptor repertoire analysis: bridging the gap with multimodal single-cell RNA sequencing. Immunoinformatics 5, 100009 (2022).
Galon, J. et al. Towards the introduction of the ‘Immunoscore’in the classification of malignant tumours. J. Pathol. 232, 199–209 (2014).
Zhao, P., Li, L., Jiang, X. & Li, Q. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J. Hematol. Oncol. 12, 1–14 (2019).
Yarahmadi, A. & Afkhami, H. The role of microbiomes in gastrointestinal cancers: new insights. Front. Oncol. 13, 1344328 (2024).
Zhao, L.-Y. et al. Role of the gut microbiota in anticancer therapy: from molecular mechanisms to clinical applications. Signal Transduct. Target. Ther. 8, 201 (2023).
Porciello, N., Franzese, O., D’Ambrosio, L., Palermo, B. & Nisticò, P. T-cell repertoire diversity: friend or foe for protective antitumor response? J. Exp. Clin. Cancer Res. 41, 356 (2022).
Li, R. et al. T-cell receptor sequencing reveals hepatocellular carcinoma immune characteristics according to Barcelona Clinic liver cancer stages within liver tissue and peripheral blood. Cancer Sci. 115, 94–108 (2024).
Borràs, D. M. et al. Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer. Cell Discov. 9, 114 (2023).
Wang, H. et al. Characterization of the T-cell receptor repertoire associated with lymph node metastasis in colorectal cancer. Front. Oncol. 14, 1354533 (2024).
Ma, R., Yuan, D., Guo, Y., Yan, R. & Li, K. Immune effects of γδ T cells in colorectal cancer: a review. Front. Immunol. 11, 1600 (2020).
Conway. J. W. et al Unveiling the tumor immune microenvironment of organ-specific melanoma metastatic sites. J. Immunother. Cancer 10, e004884(2022),
Bruni, D., Angell, H. K. & Galon, J. The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy. Nat. Rev. Cancer 20, 662–680 (2020).
Mlecnik, B. et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci. Transl. Med. 8, 327ra326–327ra326 (2016).
Li, Y. et al. Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling. Nat. Commun. 15, 7784 (2024).
Tanno, H. et al. Determinants governing T cell receptor α/β-chain pairing in repertoire formation of identical twins. Proc. Natl. Acad. Sci. USA 117, 532–540 (2020).
George, A. J., Stark, J. & Chan, C. Understanding specificity and sensitivity of T-cell recognition. Trends Immunol. 26, 653–659 (2005).
Vujovic, M. et al. T cell receptor sequence clustering and antigen specificity. Comput. Struct. Biotechnol. J. 18, 2166–2173 (2020).
Wong, W. K., Leem, J. & Deane, C. M. Comparative analysis of the CDR loops of antigen receptors. Front. Immunol. 10, 2454 (2019).
Mayer-Blackwell, K. et al. TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs. Elife 10, e68605 (2021).
Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).
Xu, X., Li, H. & Xu, C. Structural understanding of T cell receptor triggering. Cell. Mol. Immunol. 17, 193–202 (2020).
Goncharov, M. et al. VDJdb in the pandemic era: a compendium of T cell receptors specific for SARS-CoV-2. Nat. Methods 19, 1017–1019 (2022).
Jin, Z. et al. Outcome of mismatch repair-deficient metastatic colorectal cancer: the Mayo Clinic experience. Oncologist 23, 1083–1091 (2018).
Laplante, P. et al. Effect of mismatch repair deficiency on metastasis occurrence in a syngeneic mouse model. Neoplasia 62, 101145 (2025).
Yu, L. et al. Tumor-infiltrating gamma delta T-cells reveal exhausted subsets with remarkable heterogeneity in colorectal cancer. Int. J. Cancer 153, 1684–1697 (2023).
Pan, L. et al. Progress of research on γδ T cells in colorectal cancer. Oncol. Rep. 52, 160 (2024).
Wang, Y., Xu, Y., Chen, H., Zhang, J. & He, W. Novel insights based on the plasticity of T cells in the tumor microenvironment. Explor. Immunol. 2, 98–132 (2022).
Stadinski, B. D. et al. Hydrophobic CDR3 residues promote the development of self-reactive T cells. Nat. Immunol. 17, 946–955 (2016).
Stern, J. N. et al. Peptide 15-mers of defined sequence that substitute for random amino acid copolymers in amelioration of experimental autoimmune encephalomyelitis. Proc. Natl. Acad. Sci. USA 102, 1620–1625 (2005).
Vlachavas, E. I. et al. Radiogenomic analysis of F-18-fluorodeoxyglucose positron emission tomography and gene expression data elucidates the epidemiological complexity of colorectal cancer landscape. Computat. Struct. Biotechnol. J. 17, 177–185 (2019).
Ye, X. et al. High-throughput sequencing-based analysis of T cell repertoire in lupus nephritis. Front. Immunol. 11, 1618 (2020).
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
Liu, Y. et al. Immune phenotypic linkage between colorectal cancer and liver metastasis. Cancer Cell 40, 424–437.e5 (2022).
Van der Loo, M. P. The stringdist package for approximate string matching. R. J. 6, 111 (2014).
Bodenhofer, U., Bonatesta, E., Horejš-Kainrath, C. & Hochreiter, S. msa: an R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).
Zhou, L. et al. ggmsa: a visual exploration tool for multiple sequence alignment and associated data. Brief. Bioinform. 23, bbac222 (2022).
Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. Package ‘survminer’. Drawing survival curves using ‘ggplot2’(R package version 0.3.1) 2017.
Brown, S. J., Goetzmann, W. N. & Ross, S. A. Survival. J. Financ. 50, 853–873 (1995).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
Lifschitz, S. et al. Bio-strings: a relational database data-type for dealing with large biosequences. BioTech 11, 31 (2022).
Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).
Acknowledgements
This work was supported by Shandong Provincial Natural Science Foundation (No. ZR2025MS1517).
Author information
Authors and Affiliations
Contributions
Y.n.Z conceived the study, applied for ethical approval, provided administrative support, and finalized the revised manuscript. L.L. conducted data analysis and drafted the manuscript, while J.L. organized clinical data, performed statistical analyses, and prepared the figures. F.W. collected and processed specimens and contributed to manuscript drafting, and R.J. carried out data analysis and figure preparation. H.W. was responsible for specimen processing, quality control, and guiding the writing and revision of the manuscript. X.L. performed data analysis and contributed to revising the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Li, L., Li, J., Wang, F. et al. T-cell receptor clonotypic diversity and specialization in digestive system cancers. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01294-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41698-026-01294-4


