An EBV-related CD4 TCR immunotherapy inhibits tumor growth in an HLA-DP5+ nasopharyngeal cancer mouse model

Adoptive transfer of T cell receptor–engineered T cells (TCR-T) is a promising strategy for immunotherapy against solid tumors. However, the potential of CD4+ T cells in mediating tumor regression has been neglected. Nasopharyngeal cancer is consistently associated with EBV. Here, to evaluate the therapeutic potential of CD4 TCR-T in nasopharyngeal cancer, we screened for CD4 TCRs recognizing EBV nuclear antigen 1 (EBNA1) presented by HLA-DP5. Using mass spectrometry, we identified EBNA1567–581, a peptide naturally processed and presented by HLA-DP5. We isolated TCR135, a CD4 TCR with high functional avidity, that can function in both CD4+ and CD8+ T cells and recognizes HLA-DP5–restricted EBNA1567–581. TCR135-transduced T cells functioned in two ways: directly killing HLA-DP5+EBNA1+ tumor cells after recognizing EBNA1 presented by tumor cells and indirectly killing HLA-DP5–negative tumor cells after recognizing EBNA1 presented by antigen-presenting cells. TCR135-transduced T cells preferentially infiltrated into the tumor microenvironment and significantly inhibited tumor growth in xenograft nasopharyngeal tumor models. Additionally, we found that 62% of nasopharyngeal cancer patients showed 50%–100% expression of HLA-DP on tumor cells, indicating that nasopharyngeal cancer is well suited for CD4 TCR-T therapy. These findings suggest that TCR135 may provide a new strategy for EBV-related nasopharyngeal cancer immunotherapy in HLA-DP5+ patients.

Lysates were then spun down with 10,000 g at 4 °C, and supernatant fluids were isolated.
For immunopurification of HLA-DP5 ligands, 1 mg of B7/21 antibody was bound to 1 ml of rProtein G Sepharose beads (Cytiva) and incubated with the protein lysate overnight.HLA complexes and binding peptides were eluted using 10% (vol/vol) acetic acid.
The eluates were then loaded on Sep-Pak tC18 cartridges (Waters, 100 mg) and washed with 0.1% TFA and 2% ACN in 0.1% TFA, sequentially.The peptides were separated from HLA complexes on the cartridges by eluting with 32% ACN in 0.1% TFA and dried using vacuum centrifugation.To confirm the existence of EBNA1 immunopeptides being endogenously processed, both purified native immunopeptides and synthetic peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer coupled to an Ultimate 3000 RSLC nano system (Thermo Fisher Scientific).The samples were reconstituted in 0.1% formic acid and separated on a 25cm column (Aurora) at a flow rate of 300 nL/min using a 90 min gradient of the buffer A (0.1% formic acid) and B (80% ACN and 0.1% formic acid).Data was collected in a parallel reaction monitoring (PRM) mode.The full scan spectra were measured with a resolution of 60,000 within 50 ms maximum injection time, followed by MS2 scans with a resolution of 30,000 within 200 ms maximum injection time.The isolation window of the MS2 scan was set to 1 m/z, and ions within 300 to 100 m/z were triggered for the MS2 event.Acquisition time windows were adjusted individually according to the retention time of their corresponding synthetic peptides.The normalized collision energy was set as 30%.The PRM data was processed with Skyline (version 21.1) software as previously described (2).

Single-cell RNA sequencing and data processing.
Sequencing and quality control TCR135-transduced T cells from Day 0 were thawed.The CD45 + cells of the tumor and peripheral blood on Day 7 from five TCR135-T treated mice were sorted, pooled, and sent for single-cell RNA sequencing (scRNA-seq).Cellular suspensions were then loaded on a 10X Genomics GemCode single-cell instrument that generates single-cell Gel Bead-in-Emulsion (GEMs).Libraries were generated from the cDNAs with Chromium Next GEM Single Cell 5' Reagent Kits v3.1 and sequenced by Illumina NovaSeq 6000.
Raw BCL files were converted to FASTQ files, aligned, and count quantified using 10X Genomics Cell Ranger software (version 3.1.0).Cell-by-gene matrices for each sample were individually imported to Seurat (version 3.1.1)for downstream analysis.
Cells with an unusually high number of UMIs (≥8000) or mitochondrial genes (≥ 10%) were filtered out.We also excluded cells with less than 500 or more than 4000 genes.Additionally, doublet GEMs were filtered out using the tool DoubletFinder (v. 2.0.3) by using the PC distance to find each cell's proportion of artificial k-nearest neighbors (pANN) and ranking them according to the expected number of doublets.

Cell clustering and cell type annotation
The Seurat (3) package was used to perform unsupervised clustering analysis on scRNA-seq data.Briefly, gene counts for cells were normalized by library size and logtransformed.To minimize the effects of batch effect and behavioral conditions on clustering, we used the Harmony algorithm (4) to generate a batch-corrected embedding.
An integrated expression matrix was then scaled, and a principal component analysis was used for dimensional reduction.NK cells, NKT cells, CD4 + and CD8 + T cells were defined based on the average expression of signature genes as shown in Supplementary Table 2. CD4 + and CD8 + T cells were processed separately in downstream clustering and signature gene analysis.Uniform manifold approximation and projection (UMAP) was used to visualize clustering results.Bioinformatic analysis was performed using Omicsmart, a real-time interactive online platform for data analysis (http://www.omicsmart.com).

Calculation of tissue enrichment for cell clusters
The Ro/e value was used to estimate the tissue preference of each cell cluster, as previously described (5).Ro/e is the ratio of observed cell number over the expected cell number of a given combination of T cell cluster and tissue.The expected cell number for each combination of T cell clusters and tissues was obtained using a chi-squared test (SPSS).Ro/e > 1 suggests that cells of the given T cell cluster are more frequently observed in the specific tissue than random expectations.