mRNA-based precision targeting of neoantigens and tumor-associated antigens in malignant brain tumors

Abstract

Background: Despite advancements in the successful use of immunotherapy in treating a variety of solid tumors, applications in treating brain tumors have lagged considerably. This is due, at least in part, to the lack of well-characterized antigens expressed within brain tumors that can mediate tumor rejection; the low mutational burden of these tumors that limits the abundance of targetable neoantigens; and the immunologically "cold" tumor microenvironment that hampers the generation of sustained and productive immunologic responses. The field of mRNA-based therapeutics has experienced a boon following the universal approval of COVID-19 mRNA vaccines. mRNA-based immunotherapeutics have also garnered widespread interest for their potential to revolutionize cancer treatment. In this study, we developed a novel and scalable approach for the production of personalized mRNA-based therapeutics that target multiple tumor rejection antigens in a single therapy for the treatment of refractory brain tumors.

Methods: Tumor-specific neoantigens and aberrantly overexpressed tumor-associated antigens were identified for glioblastoma and medulloblastoma tumors using our cancer immunogenomics pipeline called Open Reading Frame Antigen Network (O.R.A.N). Personalized tumor antigen-specific mRNA vaccine was developed for each individual tumor model using selective gene capture and enrichment strategy. The immunogenicity and efficacy of the personalized mRNA vaccines was evaluated in combination with anti-PD-1 immune checkpoint blockade therapy or adoptive cellular therapy with ex vivo expanded tumor antigen-specific lymphocytes in highly aggressive murine GBM models.

Results: Our results demonstrate the effectiveness of the antigen-specific mRNA vaccines in eliciting robust anti-tumor immune responses in GBM hosts. Our findings substantiate an increase in tumor-infiltrating lymphocytes characterized by enhanced effector function, both intratumorally and systemically, after antigen-specific mRNA-directed immunotherapy, resulting in a favorable shift in the tumor microenvironment from immunologically cold to hot. Capacity to generate personalized mRNA vaccines targeting human GBM antigens was also demonstrated.

Conclusions: We have established a personalized and customizable mRNA-therapeutic approach that effectively targets a plurality of tumor antigens and demonstrated potent anti-tumor response in preclinical brain tumor models. This platform mRNA technology uniquely addresses the challenge of tumor heterogeneity and low antigen burden, two key deficiencies in targeting the classically immunotherapy-resistant CNS malignancies, and possibly other cold tumor types.

Keywords: Adoptive T cell therapy; Brain tumors; Cancer immunity; Glioblastoma; Immune checkpoint blockade; Personalized immunotherapy; Vaccines; mRNA therapeutics.

Fig. 1

Antigen prediction and tumor antigen-specific TOFU mRNA production. a Schematic for tumor antigen-specific TOFU mRNA vaccine development (Created with BioRender.com). b The number of neoantigens or tumor-associated antigens (TAAs) predicted for the KR158-Luc (Kluc), GL261, NSC, and Ptch tumors using the Open Reading Frame Antigen Network (O.R.A.N) pipeline (n = 3 per tumor). c–f Enrichment of tumor antigens in TOFU mRNA libraries as compared to total tumor RNA (ttRNA) for the Kluc (c), GL261 (d), NSC (e), and Ptch (f) tumors. Blue bars represent the % of TOFU genes and orange represents all other genes in the pool. g–j Volcano plots demonstrate the fold change increase in the expression of the TOFU antigens following enrichment in Kluc (g), GL261 (h), NSC (i), and Ptch (j) tumors. TOFU antigens are highlighted and labeled in each plot for all 4 models respectively. k Pie-chart showing enrichment efficiency of tumor-specific and associated genes (316 genes that were selected using differential gene expression and non-synonymous SNVs) from a human GBM patient’s tumor RNA sample. TOFU gene representation is shown in blue and other genes are in orange

Fig. 2

Demonstration of antigen-specific responses against TOFU antigens. a–d Peptide stimulation and re-challenge of T-cells from Kluc TOFU mRNA primed animals (a), GL261 TOFU mRNA primed animals (b), NSC TOFU mRNA primed animals (c), and Ptch TOFU mRNA primed animals (d) with selected predicted neoantigens and TAA peptides (n = 3 per sample). IFN-γ release is detected using ELISA after 24 h. Antigen reactivity is considered as > 100 pg/ml IFN-γ production and at least a 2-fold increase over the response to the irrelevant peptide. Statistical analysis is done using 2-way ANOVA with multiple comparisons using Bonferroni correction (two-tailed); p < 0.05 is *, p < 0.01 is **, p < 0.001 is ***, and p < 0.0001 is ****. The experiments have been repeated at least 2 times to validate the immunogenicity

Fig. 3

Therapeutic efficacy of TOFU mRNA vaccines in combination with ICI. a Timeline of therapy administration for the TOFU vaccine plus anti-PD-1 Ab combination approach. b Kluc tumor growth measurement using in vivo luminescence imaging at 48 h after the third vaccine treatment on day 20 following the tumor implantation (n = 5 per group). Statistical analysis was done using one-way ANOVA and Tukey’s multiple comparisons; p < 0.05 is *, and p < 0.01 is **. c Survival curve of Kluc tumor-bearing mice treated with Kluc TOFU mRNA vaccine plus anti-PD-1 Ab (TOFU DC + PD-1) and control treatments (Ctl DCs + IgG, Ctl DCs + PD-1, and TOFU DCs + IgG) (n = 6 to 7). d Survival curve of GL261 tumor-bearing mice treated with GL261 TOFU DCs + PD1 combination and control treatments (n = 7). Statistical analysis was performed using the log-rank (Mantel-Cox) test with significance at p < 0.05

Fig. 4

Reprograming of the tumor microenvironment following treatment with TOFU mRNA vaccines in combination with ICI. a–b Flow cytometry analysis of PD-1 expression on CD4 + and CD8 + T-cells in the peripheral blood of TOFU DCs + PD-1 or control-treated Kluc (a) or GL261 (b) tumor-bearing mice (n = 5 to 6 per group). Statistical analysis was done using one-way ANOVA and Dunnett’s multiple comparisons, p < 0.05. c–d Heatmap showing immune-cell deconvolution from the RNA-seq data of isolated CD45 + ve immune cells from the Kluc (c) and GL261 (d) tumors following treatment (n = 4 to 5). e–f Pathway-based gene expression analysis in the immune cells for Kluc (e) and the GL261 (f) tumors using the nCounter Immunology Panel from NanoString (n = 4 to 5). Statistical analysis was performed using Kruskal–Wallis and Dunn’s multiple comparison tests for TOFU DC + PD-1 to untreated mice comparison; p < 0.05

Fig. 5

Therapeutic efficacy of TOFU mRNA vaccines in combination with ACT. a Timeline for therapy administration for the TOFU vaccine plus ACT combination approach following host conditioning with 9 Gy TBI and HSCs. b Kluc tumor growth measurement using in vivo luminescence imaging at day 32 after the tumor implantation (n = 5 per group). Statistical analysis was done using one-way ANOVA and Tukey’s multiple comparisons; p < 0.05 is *, p < 0.01 is **, and p < 0.001 is ***. c Survival curve of Kluc tumor-bearing mice treated with Kluc TOFU-ACT and control treatments (n = 7 to 9). d Survival curve of GL261 tumor-bearing mice treated with GL261 TOFU-ACT and control treatments (n = 7 to 9). Statistical analysis was performed using the log-rank (Mantel-Cox) test with significance at p < 0.05. e Uniform Manifold Approximation and Projection (UMAP) of 1000 CD45 − ve single cells from the TOFU-ACT and control groups which were resolved into 8 clusters. The cell type and the percentage of cells are labeled across the respective population

Fig. 6

Reprogramming of the tumor microenvironment following treatment with TOFU-ACT. a UMAP of 15,000 CD45 + ve single cells from the TOFU-ACT and control groups which were resolved into 11 major clusters. The cell type and the percentage of cells are labeled across the respective population. b UMAP of T-cells and NK single cells from the TOFU-ACT and control groups which were resolved into 7 major clusters. The cell type and the percentage of cells are labeled across the respective population. c–d Analysis of gene expression of common T-cell exhaustion and cytotoxic markers in the three treatment groups across the different T-cell populations. The heatmap of gene expression is shown in c and the quantification of the same is shown in (d). Statistical analysis was performed using Kruskal–Wallis and Dunn’s test for multiple comparisons; p < 0.05 is *, p < 0.01 is **, p < 0.001 is ***, and p < 0.0001 is ****

Fig. 7

T-cell receptor sequencing following the TOFU-ACT treatment. a Chao E mean score analysis for determining the clonal diversity of T-cells (n = 3 per group). Statistical analysis was performed using Kruskal–Wallis and Dunn’s test for multiple comparisons; p < 0.05 is *, p < 0.01 is **, p < 0.001 is ***, and p < 0.0001 is ****. b T-cell receptor repertoire and clonal expansion using TCR-seq analysis (n = 3 per group). The distribution of clones is shown as hyperexpanded, large, medium, small, and rare based on the expansion of clones and proportion out of the total number of reads. c The expression of TCR Vβ families in the T-cell repertoire of all three treatment groups following therapy. Black arrows show the Vβ families which are either upregulated or downregulated in the TOFU-ACT-treated tumors compared to controls. d The proportion of individual T-cell clones within different TCR Vβ families. e The expression of the individual hyperexpanded clones in the TOFU-ACT treated tumors compared to the untreated and 9 Gy TBI treated tumors