A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice

Abstract

Adoptive cell transfer (ACT) using neoantigen-specific T cells is an effective immunotherapeutic strategy. However, the difficult isolation of neoantigen-specific T cells limits the clinical application of ACT. Here, we propose a method to prepare neoantigen-reactive T cells (NRT) for ACT following immunization with a tumor lysate-loaded dendritic cell (DC) vaccine. We show that the DC vaccine not only induces a neoantigen-reactive immune response in lung cancer-bearing mice in vivo, but also facilitate NRT cell preparation in vitro. Adoptive transfer of the NRTs as combinatorial therapy into DC vaccine-immunized, LL/2 tumor-bearing mice allows infiltration of the infused NRTs, as well as the enrichment of neoantigen reactive, non-ACT/NRT T cells into the tumor microenvironment with the function of these neoantigen-reactive T-cell receptors validated in vitro. In summary, we propose a method for preparing NRTs that increases ACT efficacy and paves the way to the design of personalized immunotherapies.

Fig. 1. The OCDC vaccine is effective and can trigger neoantigen-specific immune responses.

a Experimental design. b Mean tumor volume (n = 9 independent animals in each group). c Tumor weight (n = 9 independent animals in each group). d Tumor growth curves of individual mice (n = 9 independent animals in each group). e Schematic representation of the immune response of splenic lymphocytes to neoantigens after immunization with the OCDC vaccine. f Proportion of CD3 + CD8 + CD137+ cells in splenic lymphocytes stimulated with mutant peptides after immunization with the OCDC vaccine (n = 2 independent experiments). g Statistical analysis of the IFN-γ ELISpot assay results for splenic lymphocytes from different groups of mice stimulated with mutant peptides (n = 3 biologically independent samples). h Statistical analysis of the IFN-γ ELISpot assay results for mouse spleen lymphocytes stimulated with wild-type or mutant peptides (n = 3 biologically independent samples). Data were expressed as the mean ± SD (b, c, f, g and h). Ordinary one-way ANOVA with Tukey’s multiple comparisons test (b, c, g) and Unpaired t test (h) were carried out for statistical analysis. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Source data and P values are provided as a Source Data file.

Fig. 2. Preparing OCDC vaccine-induced neoantigen reactive T cells (NRT) is feasible.

a Schematic diagram of the immunogenicity verification of the screened neoantigens. b Flow cytometric results of splenic lymphocytes from each group of mice after coculture with DCs loaded with immunogenic neoantigen peptides (n = 3 biologically independent samples). c Flow cytometry results of TILs from each group of mice after coculture with DCs loaded with immunogenic neoantigen peptides (n = 3 biologically independent samples). d Uniform manifold approximation and projection (UMAP) plots of flow-sorted CD3 + CD137+ single cells from NRTs. e Circle plot showing the differentially expressed gene (DEG) expression in each cell cluster. f Dot plots showing the expression of representative genes in each cell cluster. g Triangle diagram showing the results of enrichment analysis for each cell cluster. h Dot plots showing the differentially expressed TFs in each cell cluster. i Pseudotime trajectory analysis of the sorted CD137 + T-cell subclusters. The direction of the inferred pseudotime is indicated by the arrow. j Pseudotime trajectory analysis of the sorted CD137 + T-cell subsets. The proportions of clonal cells in different cell clusters are shown in the bar graphs in the upper part of the figure. The direction of the inferred pseudotime is indicated by the arrow. Source data are provided as a Source Data file.

Fig. 3. Antitumor effects of OCDC vaccination combined with NRT adoptive transfusion.

a Experimental design. b Schematic of the vaccine immunization and T-cell infusion time points. c Tumor growth curves of individual mice (n = 12 independent animals in each group). d Mean tumor volume (n = 12 independent animals in each group). e Tumor weight (n = 6 independent animals in each group). f Survival curves (n = 6 independent animals in each group). g Representative images showing the proportion of GFP + T cells in tumor tissues of each group (n = 3 biologically independent samples). h Statistical results of the proportion of GFP + T cells in tumor tissues of each group (n = 3 biologically independent samples). i Multi-fluorescence immunohistochemistry shows the GFP-positive T cells and GFP-negative T cells in OCDC + T group and OCDC + NRT group (n = 3–4 independent fields of view from two independent experiments; where the yellow arrows represent GFP-positive T cells, and the white arrows represent other T cells in the tumor microenvironment; scale bar, 20 µm). j Representative images showing the proportion of GFP + IFN-γ + T cells in tumor tissues of the OCDC + T group and OCDC + NRT group (n = 3 biologically independent samples). k Statistical results of the proportion of GFP + IFN-γ + T cells in tumor tissues of each group (n = 3 biologically independent samples). l Representative images showing the proportion of GFP-IFN-γ + T cells in tumor tissues of each group (n = 3 biologically independent samples). m Statistical results of the proportion of GFP-IFN-γ + T cells in tumor tissues of each group (n = 3 biologically independent samples). Data were expressed as the mean ± SD (d, e, h, k, and m) and Kaplan-Meier plot (f). Unpaired t test (d), Ordinary one-way ANOVA with Tukey’s multiple comparisons test (e, m) and Log-rank (Mantel-Cox) test (f) were used for statistical analysis. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. Source data and P values are provided as a Source Data file.

Fig. 4. Response of TILs to distinct treatments.

a UMAP plots of T/NK cells, colored by cell type. b UMAP plots showing marker gene expression for cell type identification. The legend shows a color gradient of normalized expression. c Proportions of each cell type in each group, colored by cell type. d Heatmap of fifteen T/NK cell clusters with classical marker genes (bottom), the proportion of each cell subpopulation in each group (middle) and the ratio of the proportion of cells in the OCDC + NRT group to the proportion of cells in the other groups (top). e Pseudotime trajectory analysis of the CD8 + T-cell subclusters, colored by cell type. The direction of the inferred pseudotime is indicated by the arrow. f Pseudotime trajectory analysis of the sorted CD8 + T-cell subclusters, colored according to different groups. The direction of the inferred pseudotime is indicated by the arrow. g Heatmap showing the dynamic expression and clustering of marker genes associated with CD8 + T-cell subclusters over pseudotime. h Dot plots showing the dynamic expression of representative genes of CD8 + T-cell subclusters over pseudotime. i Gene Ontology (GO) enrichment analysis of T-cell functional pathway expression across treatments. j Representative images of tumor tissues from each group stained by multiplex immunofluorescence staining (n = 8–10 independent fields of view from three independent experiments; Scale bars represent 20 µm). k T-SNE plots showing the flow cytometry results for each group (n = 3 biologically independent samples). l The number of effective memory T cells (Tem) among the CD4+ and CD8 + T cells in the TIL population (n = 3 biologically independent samples). Data were expressed as the mean ± SD (l). Ordinary one-way ANOVA with Tukey’s multiple comparisons test (l) was used for statistical analysis. *p < 0.05, **p < 0.01, and ****p < 0.0001. Source data and P values are provided as a Source Data file.

Fig. 5. Identification of neoantigen-specific TCR groups.

a The TCR diversity analysis of different groups included the Shannon index, Inv. Simpson index and richness index (Chao1, ACE) Scores. b Sankey diagram showing the proportions of TCRs of each T-cell subtype in the different groups. c TCR correlation analysis among T-cell subtypes of different groups. d Schematic of single-cell TCR sequencing (scTCR-seq) (Created in BioRender. Min, D. (2024) https://BioRender.com/k17q100). e Specificity inference pipeline. f Overview of all TCR groups. The size of the dots represents whether the TCR is clonal, large dots represent clonal TCRs, small dots represent nonclonal TCRs, the colors represent TCRs from different groups, and black represents the TCR group originating from two or more groups. g The network of the TCR groups. Shared CDR3β sequences organize TCR groups into distinct communities, and the depth of the connecting lines indicates the number of shared CDR3β sequence(s) between any two connected nodes. h IFN-γ status of sorted CD137 + T cells. Magenta dots represent the IFN-γ-positive TCR groups. (i) IFN-γ status of TILs. Magenta dots represent the IFN-γ-positive TCR groups. j The neoantigen-specific TCR groups. Red indicates the neoantigen-specific TCR groups originating from the OCDC + NRT cohort. The blue and green colors indicate the neoantigen-specific TCR groups originating from the OCDC group and NS group, respectively. k Neoantigen-specific TCR groups and cluster visualization diagrams. One large neoantigen-related community is circled, and the CDR3β members of the specificity groups are highlighted with the short motifs “SLGG%DT” and “LGGQDT” in red font; heatmap showing distinct CDR3β members (columns); table showing an example of the “SLGG%DT” and “LGGQDT” specificity groups. The counts of Vβ gene usage are shown. Source data are provided as a Source Data file.

Fig. 6. Characterization of neoantigen-specific T cells.

a Specificity inference pipeline. b Overview of neoantigen-specific TCR clusters. The size of the dots represents whether the TCR is clonal, large dots represent clonal TCRs, and small dots represent nonclonal TCRs. The red dots represent groups containing neoantigen-specific TCRs. c The network of neoantigen-specific TCR clusters. Shared CDR3β sequences organize TCR groups into distinct communities, and the depth of the connecting lines indicates the number of shared CDR3β sequence(s) between any two connected nodes. d IFN-γ status of neoantigen-specific TCR clusters. The red dots indicate the IFN-γ-positive TCR groups. e The neoantigen-reactive TCR modes. A supercluster containing neoantigen-reactive TCR groups is circled, and the neoantigen-reactive TCR groups are highlighted in red font; the CDR3β members of the neoantigen-reactive TCR groups are highlighted with short motifs; a heatmap showing distinct CDR3β members (columns) of the neoantigen-reactive-related specificity groups (rows) and the counts of shared CDR3β sequences between specificity groups within the circled community; and a table showing an example of the specificity groups containing the motif (bold). The Vβ gene type is shown. The motif sequence markers corresponding to some motifs are shown on the right. f Distribution of T cells containing neoantigen-specific TCRs; red dots indicate that T cells contain neoantigen-specific TCRs. g The proportions of neoantigen-specific T cells. The outer circle represents the proportions of different cells among the sorted CD137 + T cells, while the inner circle indicates the proportions of various T cells that contained neoantigen-specific TCRs. h Volcano plot showing genes differentially expressed in neoantigen-specific T cells. Each red or blue dot denotes an individual gene passing our P value and fold difference thresholds, with red dots indicating genes with upregulated expression and blue dots indicating genes with downregulated expression. Ifng is highlighted in red font. i Triangle diagram showing the enrichment of neoantigen-specific T cells in different cells. The red dots indicate the regions where neoantigen-specific T cells are enriched. The P-values were analyzed by linear model fit from limma package (h). Source data are provided as a Source Data file.

Fig. 7. Neoantigen-specific TCR-T cells construction and functional validation.

a Experimental design (Created in BioRender. Min, D. (2024) https://BioRender.com/n63q768). b Expression of neoantigen peptide-recognizing TCRs in CD4+ Neo TCR-T cells. c Expression of neoantigen peptide-recognizing TCRs in CD8+ Neo TCR-T cells. d Apoptotic effects of Neo TCR-T cells on tumor cells (n = 3 biologically independent samples). e Tumor cells survival rates measured by real-time cell analyzer (n = 3 biologically independent samples). f Expression of Neurl4 peptide-recognizing TCRs in CD4+ Neo TCR-T cells (n = 3 biologically independent samples). g Expression of Neurl4 peptide-recognizing TCRs in CD8+ Neo TCR-T cells (n = 3 biologically independent samples). h Expression of IFN-γ in CD4+ Neo TCR-T cells (n = 3 biologically independent samples). i Expression of IFN-γ in CD8+ Neo TCR-T cells (n = 3 biologically independent samples). Data were expressed as the mean ± SD (d–i). Two-way ANOVA (d, e) and unpaired t test (f, g, h, i) were used for statistical analysis, *p < 0.05, **p < 0.01, ***p < 0.00 l and ****p < 0.0001. Source data and P values are provided as a Source Data file.

Fig. 8. Detection the myeloid cells in the TME.

a UMAP plots of myeloid cells, colored by cell type. b Circle diagram showing the DEGs in each cell cluster, colored by cell type. c Proportions of each cell type in each group, colored by cell type. d Triangle plots showing the differences in DC functions in different groups, colored according to group. e Box plots showing the enrichment scores of the activated DC signatures across groups (NS: n = 125; DC: n = 174; OCDC: n = 136; OCDC + T: n = 36; OCDC + NRT: n = 123). f Circos plot of the cellular crosstalk between DCs and other cell types in the OCDC + NRT group relative to the remaining groups. The top 30 differentially expressed DC ligands are shown. Pink represents cellular crosstalk that is upregulated in the OCDC + NRT group relative to the remaining groups, and purple represents cellular crosstalk that is specific to the OCDC + NRT group relative to the remaining groups. g Representative multiplex fluorescence immunohistochemistry images show DCs in each group (n = 3-4 independent fields of view from two independent experiments; scale bar, 20 µm). h Representative images showing the proportion of M1-like macrophages and M2-like macrophages within the TME of each group. i Representative multiplex fluorescence immunohistochemistry images show M1-like macrophages and M2-like macrophages in each group (F4/80 + CD163- represents like macrophages, and F4/80 + CD163+ represents M2-like macrophages; n = 3–4 independent fields of view from two independent experiments; scale bar, 20 µm). The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box) (e). Statistical analysis was performed by two-sided Wilcoxon rank-sum test (e), *p < 0.05, and ****p < 0.0001. Source data and P values are provided as a Source Data file.

Fig. 9. ScRNA-seq profile of tumor cells.

a UMAP plots of the tumor cells, colored by cell type. b Proportions of each cell type in each group, colored by cell type. c Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the different groups. d Circos plot of the CNV regions in different groups. e Circle plot showing the cellular crosstalk between tumor cells and other types of cells in the tumor microenvironment, comparing the OCDC + NRT group with the other groups. Pink represents upregulated cell communication in the OCDC + NRT group compared to the other groups, while purple represents specific cell communication in the OCDC + NRT group compared to the other groups. f Methodology for detecting somatic mutations in high-throughput single-cell profiling datasets (Created in BioRender. Min, D. (2024) https://BioRender.com/g96q664). g Oncogenic driver gene mutations and neoantigen mutations in cancer cells of the NS group. h Bar charts on the left showing the ratio of neoantigen mutations in each group, table on the right showing the count of mutant neoantigens and wild type neoantigens in each group. Source data are provided as a Source Data file.

Fig. 10. The discriminative marker of neoantigen-specific T cells is associated with improved survival in cancer patients.

a Heatmap of the area under the curve (AUC) showing the differences in the ability of the various prediction models to predict neoantigen-specific T cells. b Influence of the CNST score on the survival of patients with common cancer types. c Kaplan‒Meier survival curve for OS in patients with high or low CNST scores. d Kaplan‒Meier survival curve for progression-free interval (PFI) in patients with high or low CNST scores. e Kaplan‒Meier survival curve for disease-specific survival (DSS) in patients with high or low CNST scores. f CNST scores among cancer types in the TCGA database, ordered by median (ACC: n = 77; BLCA: n = 407; BRCA: n = 1091; CESC: n = 304; CHOL: n = 36; COAD: n = 286; DLBC: n = 47; ESCA: n = 181; GBM: n = 159; HNSC: n = 518; KICH: n = 66; KIRC: n = 530; KIRP: n = 288; LAML: n = 173; LGG: n = 510; LIHC: n = 369; LUAD: n = 513; LUSC: n = 498; MESO: n = 87; OV: n = 422; PAAD: n = 178; PCPG: n = 177; PRAD: n = 495; READ: n = 91; SARC: n = 258; SKCM: n = 466; STAD: n = 414; TGCT: n = 132; THCA: n = 504; THYM: n = 119; UCEC: n = 180; UCS: n = 57; UVM: n = 79). g From bottom to top: mRNA expression (median normalized expression levels); expression versus methylation (gene expression correlation with DNA methylation beta value); amplification frequency (the difference between the fraction of samples in which an IM is amplified in a particular subtype and the amplification fraction in all samples); and deletion frequency (as amplifications) for 66 IM genes by immune subtype. h Relationship between the CNST score and infiltration of most immune cells. (i) Relationships between CNST scores and immune cell infiltration in patients with different tumors. The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box) (f). The P values and hazard ratio (HR) were analyzed by Cox proportional-hazards model (b), two-sided log-rank test (c, d, e) and Pearson’s correlation analysis (h, i). Source data and P values are provided as a Source Data file.