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Concurrent delivery of immune checkpoint blockade modulates T cell dynamics to enhance neoantigen vaccine-generated antitumor immunity

Nature Cancer volume 3, pages 437–452 (2022)Cite this article

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Abstract

Neoantigen vaccines aiming to induce tumor-specific T cell responses have achieved promising antitumor effects in early clinical trials. However, the underlying mechanism regarding response or resistance to this treatment is unclear. Here we observe that neoantigen vaccine-generated T cells can synergize with the immune checkpoint blockade for effective tumor control. Specifically, we performed single-cell sequencing on over 100,000 T cells and uncovered that combined therapy induces an antigen-specific CD8 T cell population with active chemokine signaling (Cxcr3+/Ccl5+), lower co-inhibitory receptor expression (Lag3−/Havcr2−) and higher cytotoxicity (Fasl+/Gzma+). Furthermore, generation of neoantigen-specific T cells in the draining lymph node is required for combination treatment. Signature genes of this unique population are associated with T cell clonal frequency and better survival in humans. Our study profiles the dynamics of tumor-infiltrating T cells during neoantigen vaccine and immune checkpoint blockade treatments and high-dimensionally identifies neoantigen-reactive T cell signatures for future development of therapeutic strategies.

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Fig. 1: Neoantigen vaccine combined with ICB remodels TILs to induce a durable immune response.
Fig. 2: The dynamics of TILs in response to distinct immunotherapies.
Fig. 3: Lineage tracking of clonal T cell subsets is associated with immunotherapies.
Fig. 4: Identification of neoantigen-specific T cell landscape in response to combination treatment.
Fig. 5: Neoantigen vaccine and ICB coordinately mediate the antitumor immune response, depending on T cells from DLNs.
Fig. 6: The discriminative marker of antigen-specific T cells are associated with better survival in human tumors.

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Data availability

The scRNA-seq and scTCR-seq data that support the findings of this study can be accessed through the Gene Expression Omnibus under accession no. GSE178881. The TCGA database is available at https://portal.gdc.cancer.gov/. The scRNA-seq dataset for Fig. 1e can be accessed at the EMBL Nucleotide Sequence Database under accession no. PRJEB34105 (ref. 24). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The scRNA data were processed using Cell Ranger v.2.1.1 (https://www.10xgenomics.com/) and analyzed with the R package Seurat v.3.1.2 (https://satijalab.org/seurat/). Monocle v.2.0 was used to investigate the transcriptional and developmental trajectories concerning different CD8+ or CD4+ T cell clusters. iSMART was implemented to identify TCR specificity (https://github.com/s175573/iSMART). The R packages fgesa v.1.16.0 (http://bioconductor.org/packages/release/bioc/html/fgsea.html) and msigdbr v.7.2.1 (https://cran.r-project.org/web/packages/msigdbr/index.html) were used to perform the GSEA.

References

  1. Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).

    Article  PubMed  CAS  Google Scholar 

  2. Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20, 651–668 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Reinherz, E. L. αβ TCR-mediated recognition: relevance to tumor-antigen discovery and cancer immunotherapy. Cancer Immunol. Res. 3, 305–312 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zamora, A. E., Crawford, J. C. & Thomas, P. G. Hitting the target: how T cells detect and eliminate tumors. J. Immunol. 200, 392–399 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Haen, S. P., Löffler, M. W., Rammensee, H.-G. & Brossart, P. Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire. Nat. Rev. Clin. Oncol. 17, 595–610 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hu, Z., Ott, P. A. & Wu, C. J. Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nat. Rev. Immunol. 18, 168–182 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Sahin, U. et al. An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma. Nature 585, 107–112 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ott, P. A. et al. A phase Ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell 183, 347–362.e24 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Dammeijer, F. et al. The PD-1/PD-L1-checkpoint restrains T cell immunity in tumor-draining lymph nodes. Cancer Cell 38, 685–700.e8 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Ren, X. et al. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu. Rev. Immunol. 39, 583–609 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Carretero-Iglesia, L. et al. High peptide dose vaccination promotes the early selection of tumor antigen-specific CD8 T-cells of enhanced functional competence. Front. Immunol. 10, 3016 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Bode, C., Zhao, G., Steinhagen, F., Kinjo, T. & Klinman, D. M. CpG DNA as a vaccine adjuvant. Expert Rev. Vaccines 10, 499–511 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Longhi, M. P. et al. Dendritic cells require a systemic type I interferon response to mature and induce CD4+ Th1 immunity with poly IC as adjuvant. J. Exp. Med. 206, 1589–1602 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Scheiermann, J. & Klinman, D. M. Clinical evaluation of CpG oligonucleotides as adjuvants for vaccines targeting infectious diseases and cancer. Vaccine 32, 6377–6389 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459.e29 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Verma, V. et al. PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+CD38hi cells and anti-PD-1 resistance. Nat. Immunol. 20, 1231–1243 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yu, F. et al. The transcription factor Bhlhe40 is a switch of inflammatory versus antiinflammatory Th1 cell fate determination. J. Exp. Med. 215, 1813–1821 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cao, X. et al. Granzyme B and perforin are important for regulatory T cell-mediated suppression of tumor clearance. Immunity 27, 635–646 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Madi, A. et al. T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity. Genome Res. 24, 1603–1612 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Holman, P. O., Walsh, E. R. & Jameson, S. C. Characterizing the impact of CD8 antibodies on class I MHC multimer binding. J. Immunol. 174, 3986–3991 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Zhang, H. et al. Investigation of antigen-specific T-cell receptor clusters in human cancers. Clin. Cancer Res. 26, 1359–1371 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Peske, J. D. et al. Effector lymphocyte-induced lymph node-like vasculature enables naive T-cell entry into tumours and enhanced anti-tumour immunity. Nat. Commun. 6, 7114 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Salerno, F. et al. Translational repression of pre-formed cytokine-encoding mRNA prevents chronic activation of memory T cells. Nat. Immunol. 19, 828–837 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nicolet, B. P. et al. CD29 identifies IFN-γ-producing human CD8+ T cells with an increased cytotoxic potential. Proc. Natl Acad. Sci. USA 117, 6686–6696 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lo, J. A. Epitope spreading toward wild-type melanocyte-lineage antigens rescues suboptimal immune checkpoint blockade responses. Sci. Transl. Med. 13, eabd8636 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li, T. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Curtsinger, J. M., Johnson, C. M. & Mescher, M. F. CD8 T cell clonal expansion and development of effector function require prolonged exposure to antigen, costimulation, and signal 3 cytokine. J. Immunol. 171, 5165–5171 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Fairfax, B. P. et al. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26, 193–199 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Duckworth, B. C. et al. Effector and stem-like memory cell fates are imprinted in distinct lymph node niches directed by CXCR3 ligands. Nat. Immunol. 22, 434–448 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. Chow, M. T. et al. Intratumoral activity of the CXCR3 chemokine system is required for the efficacy of anti-PD-1 therapy. Immunity 50, 1498–1512.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ohtani, H., Jin, Z., Takegawa, S., Nakayama, T. & Yoshie, O. Abundant expression of CXCL9 (MIG) by stromal cells that include dendritic cells and accumulation of CXCR3+ T cells in lymphocyte-rich gastric carcinoma. J. Pathol. 217, 21–31 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Tang, H. et al. Facilitating T cell infiltration in tumor microenvironment overcomes resistance to PD-L1 blockade. Cancer Cell 29, 285–296 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tang, H. et al. PD-L1 on host cells is essential for PD-L1 blockade-mediated tumor regression. J. Clin. Invest. 128, 580–588 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lin, H. et al. Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression. J. Clin. Invest. 128, 805–815 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Park, S. L. & Mackay, L. K. Bhlhe40 keeps resident T cells too fit to quit. Immunity 51, 418–420 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Peng, Q. et al. PD-L1 on dendritic cells attenuates T cell activation and regulates response to immune checkpoint blockade. Nat. Commun. 11, 4835 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Heinonen, M. T., Kanduri, K., Lähdesmäki, H. J., Lahesmaa, R. & Henttinen, T. A. Tubulin- and actin-associating GIMAP4 is required for IFN-γ secretion during Th cell differentiation. Immunol. Cell Biol. 93, 158–166 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Liu, L. et al. Rejuvenation of tumour-specific T cells through bispecific antibodies targeting PD-L1 on dendritic cells. Nat. Biomed. Eng. 5, 1261–1273 (2021).

    Article  CAS  PubMed  Google Scholar 

  54. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kirsch, I., Vignali, M. & Robins, H. T-cell receptor profiling in cancer. Mol. Oncol. 9, 2063–2070 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank the UT Southwestern Institutional Animal Care and Use Committee and Animal Resources Center. Y.-X.F. holds the Mary Nell and Ralph B. Rogers Professorship in Immunology. This work was supported by Cancer Prevention and Research Institute of Texas (CPRIT) grant no. RR150072 given to Y.-X.F. B.L. is supported by CPRIT grant no. RR170079 and National Cancer Institute grant no. 1R01CA245318. We also thank Z. Liu, C. Han, Y. Liang, Z. Ren and A. Zhang for providing the materials used in the experiments and helpful discussions.

Author information

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  1. These authors contributed equally: Longchao Liu, Jiahui Chen, Hongyi Zhang.

Authors and Affiliations

  1. Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Longchao Liu, Casey Moore, Changzheng Lu & Yang-Xin Fu

  2. Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Jiahui Chen

  3. Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Hongyi Zhang, Jianfeng Ye & Bo Li

  4. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Yan Fang

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Contributions

L.L., B.L. and Y.-X.F. designed the study. L.L. and J.C. carried out all aspects of the research, animal care and experiments. H.Z. and Y.F. performed the scRNA-seq data analysis. L.L., J.C. and H.Z. performed the T cell phenotype analysis. L.L., J.C., H.Z., Y.-X.F. and B.L. wrote the manuscript. L.L., C.M. and B.L. revised the manuscript. J.Y. and C.L. provided the mice and important reagents. Y.-X.F. and B.L. supervised the project.

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Correspondence to Yang-Xin Fu or Bo Li.

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Nature Cancer thanks Matthew Gubin, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Neoantigen vaccine combined with ICB to induce a durable antitumor immune response.

a, Gating strategy for accessing neoantigen specific T cell phenotype. b, MC38 bearing female C57BL/6J mice were treated with two doses of neoantigen vaccine on day 10 and 17 post tumor inoculation. The percentage of PD1 + TIM3 + tetramer+ CD8 T cells in the draining lymph node and tumor was detected by flow cytometry. c, MC38 bearing female C57BL/6J mice were treated with either anti-PD-L1, adjuvant alone (Adj), the combination of adjuvant and anti-PD-L1 or neoantigen vaccine plus anti-PD-L1. Tumor volume was monitored every 3 days, P = 0.0194 (Adj + αPD-L1 vs Vaccine+αPD-L1). d, MC38 bearing female C57BL/6J mice were treated with neoantigen vaccine on day 12 post tumor inoculation. One dose of anti-PD-L1 (200 μg) was given before (day 10) or after (day 15) vaccination. The combination with two doses of anti-PD-L1 (200 μg, day 10 and 15) were used for comparison. Data were shown as mean ± s.e.m. (n = 8 (b), n = 5 (c) and n = 6 (d) mice) from two independent experiments. Statistical analysis was performed by two-way ANOVA with Tukey’s multiple comparisons test (c,d), two-tailed unpaired Student’s t-test (b), *P ≤ 0.05, ****P ≤ 0.0001.

Source data

Extended Data Fig. 2 Study design and the distribution of T cell clusters.

a, Gating strategy of single T cell sorting for single-cell sequencing. b, t-SNE plots showing the distribution of CD8 + and CD4 + T cells for each scRNA-seq library.

Extended Data Fig. 3 Expression levels of signature genes in each T cell cluster.

a, Heatmap showing the mean expression of discriminative genes for each cluster of conventional CD4 + T cells (n = 29,305 cells). b, Heatmap showing the mean expression of discriminative genes for each cluster of CD8 + T cells (n = 43,453 cells). c, Heatmap showing the mean expression of discriminative genes for each cluster of regulatory T cells (n = 8,058 cells). d, t-SNE plot of expression levels of selected genes in different clusters indicated by the colored oval corresponding to Fig. 2a. e, Bar plots showing the distribution of T cell clusters for each sample (n = 93,399 cells).

Source data

Extended Data Fig. 4 Single-cell analyses of the dynamic changes of TILs in response to distinct immunotherapies.

Bar plots displaying the dynamics of several major CD8 + T cell clusters (upper panel) and CD4 + T cell clusters (lower panel) in response to different immunotherapies (T.na (n = 2,238 cells); T.eff (n = 6,191 cells); T.ex (n = 7,050 cells); T.na (n = 2,046 cells); T.Th1 (n = 4,798 cells); T.Treg (n = 4,122 cells)). P values were determined by a chi-square test on counts of T cells, exact p values were provided in Source Data Extended Data Fig. 4.

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Extended Data Fig. 5 The dynamic change of major tumor-infiltrated T cell populations in response to distinct immunotherapies.

a, Gating strategies for accessing the major tumor-infiltrated T cell populations by flow cytometry. b, MC38 bearing female C57BL/6J mice were treated with either neoantigen vaccine, anti-PD-L1 or the combination. The percentage of CCR7 + cells in tumor infiltrated CD4 and CD8 T cells were detected by flow cytometry as indicated time points. Data were shown as mean ± s.e.m. (n = 5 mice) from two independent experiments. Statistical analysis was performed by two-way ANOVA with Šídák’s multiple comparisons test (b), ****P ≤ 0.0001.

Source data

Extended Data Fig. 6 Lineage tracking of clonal CD8 T cell subsets associated with immunotherapies.

a, Boxplots showing the clonal score of exhausted T cells (CD8−08), effector T cells (CD8-05), Tregs (CD4−04) and Th1-like T cells (CD4-06) in different treatment groups. b, Boxplot showing the pairwise transition score between CD8-05 and other intra-tumor CD8 + clusters across all tumor samples. Different T cell clusters were randomly downsampled (50%) 10 times for statistical test (n = 10 permutations (a,b)). Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. c, The trajectory of three CD8 + T cell clusters showing the inferred pseudotime along the tree-like structure. d, The trajectory of three CD8 + T cell clusters showing by consistent clones. e, The monocle component 1 correlates with the stemness score of CD8 + T cells. f, The distribution of CD8 + T cells in different transcriptional states identified by monocle across all groups. Two sided Wilcoxon rank-sum test were used for multiple groups comparisons, exact p values were provided in Source Data Extended Data Fig. 6 (a,b). Two-sided Pearson’s correlation coefficient test was used to determine the p value, P < 2.2 ×10-16 (e).

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Extended Data Fig. 7

Lineage tracking of clonal CD4 T cell subsets associated with immunotherapies. a, Monocle-guided cell trajectory showing the state transition between two major conventional CD4 + T cell clusters (CD4-03, CD4-06). b, Violin plots showing the expression level of Lag3, Havcr2, Ctla4 and Tgfb1 on the CD4 + T cells in transcriptional state 2-3. c, The distribution of CD4 T cells in the monocle-identified transcriptional states among different groups. d, Monocle-guided cell trajectory of three regulatory T cell (Treg) clusters (CD4-02, CD4-05 and CD4-07). 4 transcriptional states were identified along the inferred trajectory. e, Violin plots showing the expression level of S1pr1, Klf2, Il10 and Glrx in the two terminal transcriptional states (2 and 4). f, The distribution of Treg cells in the monocle-identified transcriptional states among different groups. g, Heat map showing the fraction of clonotypes belonging to a primary phenotype cluster (rows) that are shared with other secondary phenotype clusters (columns). h, The fraction of clonal cells in each functional state of Treg trajectory. The two sided Wilcoxon rank-sum test were used to calculate the p value following the adjustment of the Benjamini-Hochberg method to get the fdr q value, n = 6,126 cells (a-c) and n = 4,415 cells (d-f). ***represents fdr q value < 0.001 (b,e).

Source data

Extended Data Fig. 8 The landscape of neoantigen Adpgk-specific CD8 + T cells.

Female C57BL/6J mice were subcutaneously injected with neoantigen vaccine, the percentage of Adpgk-specific T cell (tetramer + ) in the draining lymph node was detected by flow cytometry. Representative data of 6 independent mice was shown.

Source data

Extended Data Fig. 9 Neoantigen vaccine and ICB coordinately mediated the anti-tumor immune response depending on T cells from draining lymph node.

a-c, female C57BL/6J mice were subcutaneously inoculated with 1×106 MC38 tumor cells and treated with either neoantigen vaccine, anti-PD-L1 or the combination. Gating strategies for accessing the phenotype of tumor infiltrated CD8 T cells by flow cytometry (a). The percentage of neoantigen-specific T cell in the tumor tissue was detected by flow cytometry. The representative result for Fig. 5b (n = 4 independent mice) was shown in (b). The percentage of IFNγ-producing CD8 T cells were detected by Elispot assay (n = 5 mice) (c). d-e, C57BL/6J mice were subcutaneously injected with neoantigen vaccine. Lymphocytes from draining lymph node were harvested at day 6 post vaccination and adoptively transferred to MC38 bearing Rag1-/- mice. Two doses of anti-PD-L1 were given to the recipient mice on day 2 and 5 post adoptive transfer. The percentage of tetramer+ cells in the donor draining lymph node was detected by flow cytometry (d). The representative IFNγ + spots for Fig. 5k were shown (e). Data were shown as mean ± s.e.m. from two independent experiments. Statistical analysis was performed by one-way ANOVA (c), ****P ≤ 0.0001.

Source data

Extended Data Fig. 10 The discriminative markers of neoantigen specific T cells are associated with better clinical outcome in human tumors.

a, The correlation between CAST score and clone size of CD8+ T cells in BCC patients. The solid red line represents LOESS fitting result (n = 26,846 cells). b, Boxplots comparing the expression of discriminative marker for CD8-05 in BCC patients’ CD8 T cells with large (n = 1,271) or small (n = 1,446) clone. Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. Two-sided Spearman’s correlation coefficient test was used to determine the p value, P < 2.2 × 10−16 (a).

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Liu, L., Chen, J., Zhang, H. et al. Concurrent delivery of immune checkpoint blockade modulates T cell dynamics to enhance neoantigen vaccine-generated antitumor immunity. Nat Cancer 3, 437–452 (2022). https://doi.org/10.1038/s43018-022-00352-7

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