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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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.
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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.
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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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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.
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).
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.
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.
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).
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).
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.
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.
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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DOI: https://doi.org/10.1038/s43018-022-00352-7
