Absence of central tolerance in Aire-deficient mice synergizes with immune-checkpoint inhibition to enhance antitumor responses

The endogenous anti-tumor responses are limited in part by the absence of tumor-reactive T cells, an inevitable consequence of thymic central tolerance mechanisms ensuring prevention of autoimmunity. Here we show that tumor rejection induced by immune checkpoint blockade is significantly enhanced in Aire-deficient mice, the epitome of central tolerance breakdown. The observed synergy in tumor rejection extended to different tumor models, was accompanied by increased numbers of activated T cells expressing high levels of Gzma, Gzmb, Perforin, Cxcr3, and increased intratumoural levels of Cxcl9 and Cxcl10 compared to wild-type mice. Consistent with Aire’s central role in T cell repertoire selection, single cell TCR sequencing unveiled expansion of several clones with high tumor reactivity. The data suggest that breakdown in central tolerance synergizes with immune checkpoint blockade in enhancing anti-tumor immunity and may serve as a model to unmask novel anti-tumor therapies including anti-tumor TCRs, normally purged during central tolerance.


Statistics
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Policy information about availability of computer code Data collection For Bulk RNAseq: RNAseq read mapping and statistical analysis of differentially expressed RNA. Raw sequence data (BCL files) were converted to FASTQ format via Illumina bcl2fastq v2.17. Reads were decoded based on their barcodes and read quality was evaluated with FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were mapped to the mouse genome (mm10) using ArrayStudio® software (OmicSoft®, Cary, NC) allowing two mismatches. Reads mapped to the exons of a gene were summed at the gene level. Differential expressed genes were identified by the DESeq2 R package (Love et al., 2014) and significantly perturbed genes were defined with fold changes of at least 1.5 in either up or down direction and with p-values less than 0.01. For scRNAseq: 387 Single-cell RNA sequencing, and read mapping. Single cell suspension of tumors were sorted for CD45+ or CD8+ 388 and collected into tubes containing PBS with 0.04% BSA. The cell suspensions were loaded on a Chromium Single Cell Instrument (10X Genomics) and RNA libraries were prepared using Chromium Single Cell 3' Library, Gel Beads & Multiplex Kit (10X Genomics). Paired-end sequencing was performed on Illumina NextSeq500 where Read 1 was used for unique molecular identifier (UMI) and cell barcode while Read 2 was used for 55-bp transcript read. Sample demultiplexing, alignment, filtering, and UMI counting were performed on Cell Ranger Single-Cell Software Suite (10X Genomics).

Data analysis
Single-cell data analysis. Single-cell analysis was carried out using version 2 of the Seurat R package (Butler et al., 2018, R Core Team, 2018. Cells with fewer than 200 genes detected or over 20% of reads mapping to mitochondrial genes were discarded from analysis. Gene expression values for each cell were normalized and scaled and variation due to cell cycle stage and mitochondrial rate were regressed out as described previously (Butler et al., 2018). The number of UMI was also regressed out to correct for variation in sampling depth of these cells. The genes used for principal component analysis (PCA) were the 1000 genes with the highest dispersion (variance to mean ratio) for genes with mean UMI between 0.0125 and 8 and variance above 0.5. Genes were divided into 20 bins of equal width based on their average expression and dispersion 14 z-scores were calculated within these bins. Cells were then partitioned into clusters (Seurat FindClusters function) and visualized using the t-distributed stochastic neighbor embedding (t406 SNE) algorithm (Seurat RunTSNE function) as described previously (Butler et al., 2018). The first 15 principal components were used to run the t-SNE dimensionality reduction. The FindClusters function was run with a resolution parameter of 0.4, resulting in 14 clusters of cells. These clusters corresponded to naïve CD8+ T cells, B cells, activated CD8+ T cells, macrophages, CD8+ effector T cells, myeloid cells, dendritic cells, natural killer cells, CD4+ regulatory T cells, plasmacytoid dendritic cells, and neutrophils. Cluster cell type identities were nature research | reporting summary October 2018 determined by examining marker genes specifically expressed more highly in each cluster (Seurat FindAllMarkers function) and expression of known immune marker genes (Seurat FeaturePlot function).
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Flow Cytometry
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