A multidimensional workflow profiling of allogeneic virus-specific T cell therapies reveals potency-linked signatures

Domain: neuroscience
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Allogeneic virus-specific T cell (VST) therapies offer distinct advantages, including scalability, rapid deployment, and manufacturing consistency, and have demonstrated efficacy in multiple clinical trials. However, identifying VST products with high therapeutic potential remains a major hurdle. Here, we present a multidimensional analytical platform that integrates in vitro and in vivo anti-viral reactivity, T cell receptor (TCR) repertoire analysis, gene expression profiling, immunophenotyping, and functional validation in a humanized mouse model. Epstein-Barr virus (EBV)-specific T cells expanded from HLA-diverse healthy donors consistently enriched for TCRs targeting EBV-encoded antigens. Transcriptomic and high-dimensional flow cytometric analyses revealed a distinct effector-associated signature. Importantly, this integrative approach uncovered correlative biomarkers of T cell potency and effector function, validated in an in vivo model of EBV-driven B cell lymphoma. These findings establish a scalable framework for the characterization of allogeneic T cell products and may inform the development of predictive metrics for in vivo efficacy.

Introduction
Adoptive cell therapies (ACT) represent a transformative modality for treating cancer, infectious diseases, and autoimmune disorders1–3. Among these, CD19-targeting chimeric antigen receptor (CAR) T-cell therapy has revolutionized the management of hematologic malignancies and is now globally approved4–6. Despite this success, autologous T-cell approaches face critical limitations, including product-to-product variability driven by the intrinsic heterogeneity of patient-derived T cells6. This variability affects phenotypic and functional properties, ultimately impacting persistence and therapeutic efficacy in vivo. These limitations may underlie the reduced success of ACT in solid tumours, where durable T-cell function is essential for efficacy1,7.

Allogeneic ACT offers a promising alternative by enabling the selection of T cells from healthy donors with defined and consistent functional attributes8–10. These off-the-shelf T cells can be banked and delivered on demand, bypassing the manufacturing delays associated with autologous approaches9,10. While concerns about graft-versus-host disease (GvHD) persist, clinical studies with partially HLA-matched allogeneic virus-specific T cells (VSTs) have demonstrated favorable safety profiles, with minimal alloreactivity11–16. Rejection of allogeneic T cells remains a challenge, particularly in immunocompetent hosts; however, strategies such as HLA knockdown and gene editing are being pursued to enhance persistence9,10. In immunocompromised post-transplantation settings, these therapies have shown marked efficacy, particularly against viruses that establish lifelong latency, including Epstein-Barr virus (EBV), cytomegalovirus (CMV), BK polyomavirus, and adenovirus17,18.

EBV infects 90–95% of the global population and typically establishes latency in B cells following primary infection19,20. While most infections are asymptomatic in early childhood, delayed exposure in developed countries often results in infectious mononucleosis during adolescence21. EBV latency is tightly regulated and linked to B cell differentiation states, with immune control largely mediated by cytotoxic CD8⁺ T cells22. Groundbreaking studies by Heslop and colleagues demonstrated that donor-derived, virus-specific T cells can effectively treat EBV-associated post-transplant lymphoproliferative disorders (PTLD)23,24. Further studies by Haque et al. showed that partially HLA-matched, third-party EBV-specific T cells can be safely used with minimal GvHD risk—only one out of 57 hematopoietic stem cell transplant recipients experienced GvHD reactivation after treatment25,26.

In this study, we describe a multi-omics platform to evaluate the phenotypic and functional attributes of allogeneic EBV-specific T cells, incorporating T cell receptor repertoire analysis, transcriptional profiling, and high-content flow cytometry. This comprehensive approach defines key correlates of potency in cytotoxic T lymphocytes and provides a foundational framework for linking product characteristics to clinical outcomes in EBV-associated diseases.

Results
Expansion and functional characterization of allogeneic EBV-specific T cells
Our EBV-specific allogeneic T-cell immunotherapy is designed to contain a selective enrichment of CD8+ T cells specific for the EBV-encoded latent cycle proteins, latent membrane protein (LMP) 1 and 2, and EBV Nuclear Antigen (EBNA) 1. To establish broad human leukocyte antigen (HLA) coverage, a repository of 21 batches of allogeneic EBV-specific T cells was generated from the peripheral blood mononuclear cells (PBMC) of donors recruited through the National Marrow Donor Program (Minneapolis, MN). T cells were expanded using a single stimulation with the AdE1-LMPoly vector, as previously described27,28, cultured for 17 or 20 days in the presence of interleukin-2 (IL-2), and then cryopreserved in aliquots of 3 × 107/cells per vial. To provide an increased yield for certain common HLA types, some donors were used to generate multiple batches. Fifteen of the batches contained EBV-specific CD8+ T cells restricted through at least two HLA-alleles (Fig. 1A). The other six contained T cells restricted to a single HLA. Fifteen batches contained CD8+ T cells specific for LMP2, 10 batches were specific for LMP1, and 12 were specific for EBNA1.

To assess the key functional attributes of EBV-specific T cells that are likely to be associated with cellular potency, antigen-specific T cells in each batch were assessed for polyfunctional cytokine profile using a multiparametric intracellular cytokine assay (Fig. 1B, Supplementary Fig. 1A), proliferative potential using a cell trace violet proliferation assay (Fig. 1C, Supplementary Fig. 1B) and cytotoxic potential using an xCELLigence real time cytotoxicity assay (Fig. 1D). Most EBV-specific T cells displayed a polyfunctional profile typically associated with virus-specific CD8+ T-cell populations, characterised by the co-expression of TNF, IFN-γ and degranulation (CD107a expression), with a subset of cells co-expressing IL-2 (Fig. 2A, Supplementary Table 1). All EBV T-cell therapy batches displayed HLA-restricted cytotoxic and proliferative potential (Fig. 2B) against EBV-transformed lymphoblastoid cell lines (LCL). Correlative analysis revealed a significant association between cytotoxic potential and the proportion of CD107a+CD8+ T cells (Fig. 2C). Although less significant, a correlation was also observed between cytotoxic potential and IFN-γ+ or TNF+ CD8+ T cells. A significant correlation was not observed between IL-2+ CD8+ T cells and cytotoxic potential, nor between proliferation and any of the other functional attributes.

TCR clonotypic landscape of in vitro expanded allogeneic EBV-specific T cells
To assess TCR identity and clonal diversity in the EBV-specific T-cell therapy batches, we used a cytokine capture (IFN-γ+TNF+) approach to define EBV-specific CD8+ T cells. To induce cytokine secretion, T cells were stimulated with a pool of peptide epitopes encoded in the AdE1-LMPpoly vector. TRBV CDR3 sequencing was then performed using the ImmunoSEQ platform on DNA isolated from IFN-γ+TNF+ CD8+ T cells and total T cells from each batch, and from PBMC CD8+ T cells prior to expansion. The frequency of EBV-specific TRBV CDR3 was then compared between each batch and PBMC CD8+ T cells to assess clonal expansion. A summary of the TRBV CDR3 analysis is provided in Supplementary Table 2. All batches displayed a dramatic increase in the proportion of EBV-specific CD8+ T cells relative to the starting PBMC (Fig. 3A). The EBV-specific T cells increased from a median of 0.4% (range 0.1–1.3%) in the PBMC CD8+ T cells to a median of 63.4% (range 40.5–90.6%) in the final product. We also noted a strong correlation between the TRBV CDR3 frequencies in the starting PBMC CD8+ T cells with the proportion in the product CD8+ T cells (Fig. 3B). Although we noted a consistent increase in the absolute proportion of TRBV CDR3 sequences in the EBV T-cell therapy batches, there were clear differences in the repertoire diversity between batches. Both Shannon Diversity (Fig. 3C) and Pielou Evenness (Fig. 3D) analyses demonstrate that while most batches contain a diverse TCR profile, TIG.K.1 and TIG.K.2, which were generated from the same starting material, contain limited diversity associated with a single dominant TRBV CDR3 sequence.

To investigate the relationship between T-cell identity in different batches, we compared TRBV CDR3 usage between all batches sequenced in the study. We used a circos plot to display the sharing of TRBV CDR3 sequences from batches made from the same starting material and batches made from different starting materials (Fig. 4A). The outer band in the circos plot represents the batches made from the same starting material, and the inner band represents individual products. The number of TRBV CDR3 sequences shared between products is represented by the thickness of the ribbons. We observed a high level of concordance between batches generated from the same starting PBMC, whereby the frequency of dominant TRBV CDR3 sequences was maintained between batches (Fig. 4A). These observations also revealed sharing between unrelated batches, likely associated with shared HLA-restriction and EBV epitope reactivity. To further study the relationship between batches, we employed GLIPH analysis to define motifs within the whole library of EBV-specific TCR. In this analysis we identified several patterns associated with HLA and epitope specificity (Fig. 4B). These analyses revealed that the TRBV CDR3 sequences were dominated by a single motif, QGG, which was previously reported to be associated with the HLA-A*02:01 restricted LMP2-encoded epitope FLYALALLL29 and was present in 11 batches restricted through HLA-A*02:01. We also noted two additional motifs that were shared in unrelated batches, YAG that was present in 6 batches with shared HLA-A*02:01 restriction and RTGA that was present in 5 batches with shared HLA-B*40:01 restriction. These observations further emphasize the consistent nature of the EBV-specific T-cell therapy batches.

Phenotypic profiling of in vitro expanded allogeneic EBV-specific T cells
To assess key phenotypic attributes of EBV-specific T-cell therapy batches, we established a 25-color flow cytometry panel to study the differentiation status and trafficking potential of 21 EBV-specific T-cell therapy batches (Supplementary Table 3). When available, batches were co-stained with EBV-specific HLA-matched MHC-multimers (Supplementary Table 4). Samples were gated on CD45+CD3+CD8+ viable cells using FlowJo software (Supplementary Fig. 2), then Uniform Manifold Approximation and Projection (UMAP) and FlowSOM clustering analysis were performed on concatenated samples using OMIQ software. Marker expression patterns following UMAP analysis are shown in Supplementary Fig. 3. Clustering analysis of all cells is shown for both CD8+ T cells and MHC-Multimer specific cells in Fig. 5A (individual CD8+ T-cell clustering profiles for each batch are shown in Supplementary Fig. 4). Overlay analysis of MHC-multimer specific T cells on CD8+ T cells for each batch is also shown (Fig. 5B, Supplementary Fig. 5). While we saw uniform expression of some key differentiation makers, particularly CD49d, clustering was defined by the differential expression of other key differentiation makers, including CD27, CD28, CD57, CD62L and KLRG1 (Fig. 5C). Assessment of the proportion of both total CD8+ T cells and MHC-Multimer specific T cells demonstrated a consistent profile between batches generated from the same donor (Fig. 5D, E), which was consistent with our observations when assessing the functional profile and TRBV CDR3 repertoire. We did note some differences in our analysis, whereby TIG.A.1, TIG.B.1, TIG.B.2, TIG.G.1 and TIG.H.1 contain a high proportion of MHC-multimer specific T cells in cluster 1, which displayed high levels of CD28 but low levels of CD57 and CD27, in contrast to the remaining batches which were predominantly in cluster 4 and 5, that contain cells co-expressing CD28, CD57 and/or CD27.

Gene expression profiling of in vitro expanded allogeneic EBV-specific T cells
To broaden our assessment of the functional and phenotypic profile of EBV-specific T-cell therapy batches, we employed a custom-designed gene expression panel using the NanoString nCounter gene expression platform. Gene expression profiling was performed on unstimulated CD8+ T cells, enriched CD8+ T cells stimulated with EBV peptide epitopes, EBV-specific T cells enriched using cytokine capture (IFNγ+/TNF+) following stimulation with EBV peptide epitopes and EBV-specific T cells enriched with HLA-peptide multimers specific for EBV epitopes (Supplementary Table 4). The T-cell therapy batches displayed a high degree of correlation in their gene expression profiles with batches generated from the same PBMC, clustering close by in a Spearman correlation plot of all batch pairs (Fig. 6; Supplementary Data 1). The Spearman correlation was also consistent with the phenotypic profiling analysis, whereby TIG.A.1, TIG.B.1, TIG.B.2, TIG.H.1 and TIG.G.1 clustered together. This pattern was consistent in both unstimulated (Fig. 6A) and stimulated (Fig. 6B) CD8+ T cells, and following cytokine capture (Fig. 6C) or HLA-peptide multimer enrichment (Fig. 6D). Stimulation was also associated with strengthening of the correlation between lots, with an enriched cluster of lots in the centre of the Spearman plot. This is likely a consequence of these lots having a consistent activation profile. Clustering patterns were predominantly driven by differences in gene expression of key effector molecules. In unstimulated CD8+ T cells (Fig. 7A), the TIG.A.1, TIG.B.1, TIG.B.2, TIG.H.1 and TIG.G.1 cluster was associated with reduced expression of granzymes (GZMA, GZMK, GZMH), perforin (PRF), granulysin (GNLY) and Natural Killer Cell Granule Protein 7 (NKG7), all of which are involved in cytotoxicity. Conversely, this cluster was associated with the increased expression of genes involved in glycolysis, including ALDOA, PGAM1, LDHB and TPI1A. This cluster in stimulated CD8+ T cells (Fig. 7B) was associated with increased expression of the interleukins -32 (IL32), -13 (IL13), -5 (IL5) and -8 (CXCL8). The signature following cytokine capture was consistent with the profile seen in stimulated CD8+ T cells (Fig. 7C). The profile in HLA-peptide multimer enriched T cells was consistent with the unstimulated CD8+ T cells, characterised by lower expression of effector molecules and higher expression of glycolysis-related genes in the TIG.A.1, TIG.B.1, TIG.B.2, TIG.H.1 and TIG.G.1 cluster (Fig. 7D). The observations of our phenotyping and gene expression analysis suggested that our T cells could be grouped into two subsets, the first subset displaying a central memory like phenotype (higher CD28 expression) that was associated with lower effector potential and higher glycolysis, while the other subset could be characterised with an effector memory like phenotype (higher CD57 expression) associated with increase effector gene expression and lower glycolysis.

We next explored the relationship between the attributes of the EBV-specific T-cell therapies. T cell products were given a qualitative score (0, 1 or 2) for Degranulation, Cytotoxic Potential, Division Index, Shannon Diversity and Pielou Evenness; or a categorical score (1 or 2) for Cell Phenotype and Gene Signature (Supplementary Table 5). Some T cell products did not have complete datasets and were therefore not included in this analysis. Ring diagrams demonstrate the relationship between these parameters (Fig. 8). This analysis demonstrated that a high degranulation score was consistently associated with an effector memory-like phenotype and a high effector gene signature. While the majority of products demonstrated a high score in at least one parameter, TIG.G.1 did not, and scored poorly overall. To provide further insight into the association between gene expression and functional attributes, we used principal component analysis (PCA). PCA analysis using the top-correlating genes with these features (see Methods) showed the batches spread out across the plane of top-two PC dimensions, with batches showing high proliferation index—e.g. TIG.J.1, TIG.J.2, TIG.E.2 and TIG.E.3—clustered in the same half of the PCA, but away from those with lower proliferation index—e.g., TIG.K.1, TIG.K.2 and TIG.K.3 (Fig. 9A). T-cell therapies manufactured from the same PBMC further clustered close by (e.g., TIG.E.1, TIG.E.2 and TIG.E.3), reiterating their uniformity in proliferation and supported by similarities in their gene-expression profiles. ITGAM, TBX21, CXCR3, and CD226 were some of the genes associated with the high proliferating batches of EBV-specific T cell therapies. Likewise, batches with high CD107a clustered in the same half of the PCA plot, with sister batches clustering close by, and these were associated with high expression of genes such as GZMK, NKG7, EOMES, and XCL (Fig. 9B). While TIG.G.1 had a gene expression profile similar to the TIG.B.1, TIG.B.2 and TIG.A.1, it clustered independently in the correlation with CD107a and Division Index and was placed at ‘extreme bottom left corners’ of these PCA plots. The clustering in both settings was associated with differential expression of the FLNA gene that encodes Filamin A. Gene expression profiles also correlated with Shannon TCR diversities of the batches. In unstimulated cells, low TCR diversity was associated with expression of CXCR6, GZMA, CD96, XCL1, CCL4 and CCR1 (Fig. 9C). In stimulated CD8+ T cells batches with high TCR diversity, such as TIG.B.1 and TIG.B.2, were associated with expression of cytokines and chemokines, including IL3, CXCL8, CSF2, IL21, and CCL3 (Fig. 9D).

Pre-clinical assessment of in vitro expanded allogeneic EBV-specific T cells
We next sought to assess the impact the unique transcriptional and functional profile of EBV-specific T cells has upon their potential potency. We selected four batches, TIG.B.2, TIG E.3, TIG.J.2, and TIG.G.1, that all contained T cells restricted via HLA-A*02:01. These batches were selected due to their different parameter scores and unique clustering patterns in the PCA. To assess the potential potency of different batches, we used a xenogeneic EBV-LCL tumour model. The EBV-LCL selected was matched to all four T cell therapy batches by HLA-A*02:01 restriction alone. NOD/Rag1γC knockout mice were injected subcutaneously in the flank with 2 × 107 EBV-LCL (Fig. 10A). Mice received 2 × 107 T cells intravenously on days 6 and 12 after EBV-LCL inoculation. Routine monitoring of these animals showed no evidence of adverse effects of T-cell therapy, including weight loss or graft-vs-host disease. After 6 weeks, mice were sacrificed and organs assessed for EBV DNA and human cell subsets (Fig. 10A). Although all four T cell therapy products contained similar antigen-specificity, including the presence of the shared FLY-specific TRBV CDR3 motif (QGG, see Fig. 4B), there was discordance in their ability to control the outgrowth of EBV-transformed B cells in vivo. While mice treated with TIG.B.2, TIG.E.3 and TIG.J.2 showed significant reduction in EBV DNA in the spleen (Fig. 10B), liver (Fig. 10C) and brain (Fig. 10D), we saw no significant differences in the EBV DNA load from mice treated with T cell therapy batch TIG.G.1 when compared to the control animals who were untreated, although there was trend towards a reduction in the EBV DNA titre (Fig. 10B–D). This was consistent with the number of human B cells in the spleen and liver in these mice (Supplementary Figs. 6 and 7). Mice treated with TIG.B.2, TIG.E.3 and TIG.J.2 also displayed significant reconstitution of human CD3+CD8+ T cells (Fig. 10E, Supplementary Fig. 8), human CD3+CD4+ T cells (Fig. 10F, Supplementary Fig. 8) and EBV-specific cytokine-secreting cells (Fig. 10G) that were not evident in mice treated with TIG.G.1. These observations demonstrate that, despite the similar EBV-specific antigenic reactivity of T cells, multidimensional profiling can reveal differences that impact potential potency in vivo.

Methods
Ethics statement
This study complied with all relevant ethical regulations. The study was approved by the QIMR Berghofer Medical Research Institute Human Research Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki. All donors enrolled in the study provided informed written consent.

EBV-specific T-cell therapy manufacturing
Peripheral blood mononuclear cells (PBMC) used for manufacturing EBV-specific T-cell therapy were isolated following leukapheresis of EBV-seropositive donors. Donors were selected from the National Marrow Donor Program (NMDP) based upon human leukocyte antigen (HLA) type (Supplementary Table 6). The AdE1-LMPpoly vector used to stimulate the expansion of LMP1&2 and LMP2 and EBNA1-specific T cells has been described previously. To generate EBV-specific T-cell therapy, PBMC were mixed at a ratio of 7:3 with autologous irradiated PBMC previously infected for 1 hour with AdE1-LMPpoly at a multiplicity of infection (MOI) of 10:1. PBMC were cultured at 37°C/6.5% CO2/95% RH in G-Rex-10 flasks in RPMI 1640 supplemented with 5% human AB serum (RPMI-5% AB) at a cell density of 2 × 106 cells/cm2. Cultures were supplemented with 120 IU/mL of recombinant interleukin (IL)-2 on day 2 and 5. On day 8, cells were enumerated and reseeded at a cell density of 2–3 × 106 cells/cm2 in RPMI-5% AB supplemented with 120 IU/mL IL-2 in G-Rex-10 or G-Rex-100 flasks. Cells were reseeded at 2–3 × 106 cells/cm2 every 3 days until harvest. On the day of harvest, cells were washed, enumerated, and then cryopreserved in single-use vials at 3 × 107 cells in 10% DMSO/5% human serum albumin.

Multiparametric intracellular cytokine assay
EBV-specific T-cell therapy batches were thawed in RPMI 1640 supplemented with 10% fetal bovine serum (RPMI-10% FBS) and 120 IU/mL and allowed to recover overnight at 37°C/6.5% CO2/95% RH. T cells were stimulated with a custom peptide pool containing all epitopes encoded in the AdE1-LMPoly vector (all peptides) or with individual HLA-matched epitopes (Supplementary Table 7), and then cultured in the presence of anti-CD107a–FITC (BD Biosciences, clone: Ber-ACT8, 1:100 dilution), GolgiPlug (BD Biosciences, Catalog Number 555029) and GolgiStop (BD Biosciences, Catalog Number 554724). After 4 hours, cells were washed and incubated with anti-CD4–Pacific Blue (BD Biosciences, clone: RPA-T4, 1:25 dilution), anti-CD8–PerCP-Cy5.5 (eBioscience, clone: RPA-T8, 1:25 dilution) and Live-Dead Near IR (ThermoFisher, 1:250 dilution). Cells were then fixed and permeabilised using Cytofix/Cytoperm (BD Biosciences, Kit Catalog Number 554714), washed with Perm/Wash (BD Biosciences, Kit Catalog Number 554714) and incubated with anti–IFN-γ–AlexaFluor 700 (clone: B27, 1:50 dilution), anti-IL-2-PE (clone: MQ1-17H12, 1:50 dilution), and anti-TNF–APC (clone: Mab11, 1:200 dilution) (all from BD Biosciences). Cells were acquired using a BD LSRFortessa with FACSDiva software (BD Biosciences), and post-acquisition analysis was performed using FlowJo software (BD Biosciences). To assess the correlation between cytokine expression and other functional parameters, we performed a simple linear regression using GraphPad Prism and used Pearson to calculate the coefficient of determination (r2).

T-cell proliferation assay
EBV-specific T-cell therapy batches were thawed in RPMI-10% FBS and allowed to recover overnight at 37°C/6.5% C02/95% RH. T cells were labelled with Cell Trace Violet (ThermoFisher, Catalog Number, C34557) according to the manufacturer’s instructions, then incubated at 37°C/6.5% CO2/95% RH in triplicate at a T cell: target ratio of 1:1 with irradiated EBV-transformed LCL matched to the EBV-specific T-cell therapy batch by a single HLA allele. As a control, T cells were also incubated with autologous LCL where available, or with near HLA-matched allogeneic LCL (Supplementary Table 8). On Day 7, T cells were harvested and then stained with anti-CD8–PerCP-Cy5.5 (clone: RPA-T8, 1:400 dilution), anti-CD3 APC (BD Biosciences, clone: SKL, 1:50 dilution), anti-CD4 FITC (clone: RPA-T4, 1:25 dilution) and anti-CD19 PECy5 (BD Biosciences, clone: HB19, 1:25 dilution). Cells were acquired using a BD LSRFortessa with FACSDiva software, and post-acquisition analysis was performed using FlowJo software (BD Biosciences). The division index for each batch was calculated as the Number of Divisions/the number of CD8+ T cells at the start of culture.

Real-time T-cell cytotoxicity assay
EBV-specific T-cell therapy batches were thawed in RPMI-10% FBS containing 120 IU/mL IL-2 and allowed to recover overnight at 37°C/6.5% CO2/95% RH. Prior to assay set-up, viable T cells were isolated using a Ficoll-Paque (GE Healthcare, Catalog Number GE17144003) density gradient. The xCELLigence RTCA MP Instrument (ACEA Biosciences) was then used to measure the cytotoxic potential of EBV-specific T cell therapy batches against HLA-matched LCL pulsed with or without AdE1-LMPpoly encoded peptides. Two complete HLA-mismatched targets were used as controls (Supplementary Table 9). All EBV-transformed LCL targets were generated in-house from healthy volunteers. LCL targets were seeded at a density of 1 × 105 cells per well of an E-plate (Aligent, Catalog Number 5232368001) coated with anti-CD40 antibody (Miltenyi Biotec, Catalog Number 130-108-041) to ensure cell-tethering to the plate. The plates were then equilibrated in the incubator for 30 minutes and then transferred to the instrument inside a 37°C incubator for 5 hours prior to the addition of T cells. T cells were added to the appropriate wells in triplicate at an effector: target ratio of 1:1. The E-plates were kept in the laminar flow cabinet for 30 minutes before being transferred to the RTCA instrument. Data acquisition was resumed, and the cell index value was measured for 60 hours. Area under the curve (AUC) for each of the conditions is acquired from the xCELLigence RTCA Software Pro.

Polychromatic T-cell phenotype analysis
EBV-specific T-cell therapy batches were thawed and immediately assessed for cellular phenotype using a custom-designed antibody panel (Supplementary Table 3). T cells were first stained with Live-Dead NIR at room temperature for 15 minutes, washed, then, when available, stained with MHC-multimers for 10 minutes (Supplementary Table 4). Anti-S1PR1 was then added and cells incubated for 10 minutes, followed by anti-CCR1, CCR2, CCR4, CCR5, CXCR6, CX3CR1, and KLRG1 for a further 10 minutes at room temperature. The remaining antibodies were added and cells incubated for a further 30 minutes at 4°C. The cells were then washed, fixed with BD Cytofix fixation buffer (BD Biosciences, Catalog Number 554655), washed again and resuspended in PBS containing 2% foetal bovine serum. Cells were acquired on a Cytek Aurora laser spectral flow cytometer using SpectroFlo software as per manufacturer instructions. Unmixed FCS files were exported for analysis. Batches were run in three separate experiments. For each experiment, a reference sample of T cells was used to control for batch effects. Data analysis was initially performed using FlowJo software to gate on CD8+ T cells in each batch (Lymphocytes/Single Cells/CD14−/CD19−/CD45+/CD3+/CD4−/CD8+). Gated CD8+ T cells were exported for subsequent analysis using OMIQ (Omiq Inc., Santa Clara, CA). OMIQ was used to perform UMAP on concatenated files (UMAP settings: Neighbours=50, Minimum Distance=0, Components=2, Metric=Euclidean, Learning Rate=1, Epochs=200, Random Seed=6828, Embedding Initialization=Spectral; 13 features = CD27, CD56, CD16, CCR5, CD49f, CD28, CCR4, CD57, CCR1, CD62L, CCR2, CXCR6, KLRG1). FlowSOM was then performed on the UMAP algorithm (number of metaclusters=6). Categorical filters were then created for each metacluster, and single marker gating was performed.

TRBV CDR3 sequencing analysis
EBV-specific T-cell therapy batches were thawed in RPMI-10% FBS and allowed to recover overnight at 37°C/6.5% C02/95% RH in RPMI-10% FBS containing 120 IU/mL IL-2. IFN-γ and TNF-producing CD8+ EBV-specific T cells were isolated using the following method. The cells were activated by pulsing 10% of the cell suspension (stimulators) with a pool of EBV epitopes encoded in the AdE1-LMPoly vector for 1 hour, then the stimulators were washed twice with RPMI-10% FBS and incubated with the remaining EBV-specific T-cells (responders). After four hours, IFN-γ-FITC (Miltenyi Biotech, Catalog Number 130-090-433) and TNF-APC (Miltenyi Biotech, Catalog Number 130-091-267) capture assays were performed according to the manufacturer’s instructions. Cells were stained with anti-CD8–PerCP–Cy5.5 (clone: RPA-T8, 1:400 dilution), anti-CD4–Pacific Blue (clone: RPA-T4, 1:25 dilution), anti-CD3–Pecy7 (clone: SKL, 1:50 dilution), and LIVE/DEAD Fixable Near-IR. CD3+CD8+CD4-IFN-γ+TNF+ viable cells were sorted using a BD FACSAria III. To isolate total CD8+ T cells from both EBV-specific T-cell therapy batches and starting PBMC, cells were incubated with anti-CD8–PerCP–Cy5.5, anti-CD4–Pacific Blue, anti-CD3–Pecy7, and LIVE/DEAD Fixable Near-IR. CD3+CD8+CD4– viable lymphocytes were sorted using a BD FACSAria III (Becton Dickinson). DNA was isolated from the sorted cells using Qiagen DNeasy Kit (Qiagen, Catalog Number 69506) and then sent to Adaptive Biotechnologies for TRBV CDR3 deep sequencing analysis using the immunoSEQ platform. A summary of the TRBV CDR3 sequencing data is provided (Supplementary Table 2). Enrichment of EBV-specific TRBV CDR3 was assessed using the ImmunoSEQ Analyzer. Only clonotypes with a minimum of 5 template reads were included in the analysis. Significance was determined using a 2-sided binomial test with Benjamini-Hochberg multiple-comparisons correction, where α=0.01. To assess the correlation between CDR3 sequences in PBMC and batches, a 2-tailed Pearson correlation was used. An intersection dataframe for N=17 unstimulated batches was computed between TCR sequencing CD8 data and significantly expanded CDR3 data. Among the common CDR3 sequences that were identified, the CDR3 sequences were replicated depending on the number of templates. Random sampling without replacement was performed 10,000 times for a sample size of 1,000 CDR3 sequences each time. For each sampling, Shannon Diversity and Pielou Evenness were calculated. All the 10,000 samples were visualized using Box plots and Distribution histograms by taking the average of the samples. Motif analysis was also performed on GLIPH, which returned significant motif lists and TCR convergence groups, and WebLogo was used to design sequence logos for those significant motifs.

T-cell gene expression analysis
T cells were prepared and sorted from T-cell therapy batches for gene expression analysis as outlined above for TRBV CDR3 sequencing analysis. In addition to sorting stimulated IFN-γ and TNF-producing EBV-specific CD8+ T cells and non-stimulated total CD8+ T cells, stimulated total CD8+ T cells and EBV-specific MHC-Multimer+ T cells were also sorted from each batch. RNA was extracted from each sample and stored at −70°C until gene expression analysis was performed. Gene expression analysis was conducted using the NanoString nCounter gene expression platform (NanoString Technologies) according to manufacturer’s instructions. A custom codeset consisting of 326 genes involved in T-cell biology, immune regulation, and immune cellular markers was used. The samples were scanned at maximum scan resolution on the nCounter Digital Analyzer and gene expression data normalized by housekeeping gene expression. Differentially expressed genes were calculated using t tests with Benjamini–Hochberg multiple test correction (FDR<0.05), and represented as a heatmap using the pheatmap R package. Top 20 gene lists that correlated with EBV-specific T-cell therapy batches features namely, proliferation index, CD107a marker expression, and Shannon TCR diversity of unstimulated and stimulated cells were calculated using Spearman correlation. These gene lists were used to ‘layout’ the product batches in two-dimensional principal-component analysis plots for the batch features, using the Factoextra R package. Principal Component Analysis was used to cluster the TIG products based on their NanoString gene expression profiles. Log2-normalized gene expression profiles were used to derive the top two PCA components. The PCA plot shows the relative placement of the TIG products on a two-dimensional plane representing the two PCA components and the key genes that contribute to the components. Product features, namely, proliferation, CD107a, and TCR diversities, were overlaid on the TIG products on the PCA plot.

In vivo mouse adoptive T-cell therapy model
All animal studies were approved by the QIMR Berghofer Medical Research Institute Animal Ethics Committee under P2337A1708-614M (for breeding) and P3734A2111-638M (for experimental studies) and performed in strict accordance with the Australian Code for the Care and Use of Animals for Scientific Purposes. All animals were housed at the QIMR Berghofer Medical Research Institute Animal Facility in OptiMICE caging on a 12-hour light–dark cycle at 25°C. Dried granule food was sterilised by irradiation. The mice had free access to food and acidified water. Six-eight-week-old female NOD/Rag1 γC knockout mice were injected subcutaneously in the flank with 2 × 107 HLA-A2+ EBV-LCL. On day 6, tumours were measured, and mice were randomized into different groups (6 animals in each group) to ensure equal tumour volumes before T-cell injections. Test group animals were treated with 2 × 107 T cells from batches TIG.B.2, TIG.E.3, TIG.G.1 and TIG.J.2. Control mice were untreated. Mice received a second dose of the matching T cell batch 6 days later. Mice were then monitored for six weeks for tumour growth and well-being. Tumours did not exceed the approved maximal tumour burden of 1cm3 at the site of injection. Mice were then euthanized by CO2 asphyxiation, and organs (spleen, liver and brain) and blood were harvested. Organs were then assessed for the presence of EBV DNA using qPCR as previously described, and for human immune cell subsets by flow cytometry and intracellular cytokine analysis. Blood was assessed for the presence of human T-cell subsets. The experiments were performed twice, and statistical analysis was performed using all treated mice (n=12 per group). Statistical comparisons were performed using a Kruskal–Wallis test with Dunn’s multiple comparisons test. Data was considered statistically significant if p<0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nIn this study, the authors presented a multidimensional analytical platform to characterize allogeneic donor-derived EBV-\nspecific T cell batches, with the goal of identifying attributes linked to therapeutic potency. They reported consistent\nenrichment of potent effector-like T cells across donors though some variability in proliferative and cytolytic capacity\nappeared to be donor-intrinsic. However, the authors would need to establish a definitive prediction score derived from the\nmultidimensional analytical platform with in vivo assay in order to provide convincing evidence of its utility in the\ncharacterisation of allogeneic EBV-specific T cell therapies.\nMajor comments on data, methodology and analytical approach, data interpretation and conclusion:\n1. Figure 2A: This panel showed the proportion of T cells expressing specific markers. We assume the data were presented\nas raw proportions and not scaled. To better highlight the polyfunctional characteristics across therapy batches, it might be\nmore informative to scale the values per marker across products within each HLA restriction group. This normalization would\nmake it easier to compare relative marker expression and functional diversity across different batches.\n2. Figure 2 and 8: It is unclear whether the correlation analyses were based on the correlation coefficient (r) or the coefficient\nof determination (r²), as the methods provided limited detail. While the figure labels suggested r², the context implied that r\nwas used. This distinction is critical as r and r² convey different meanings, i.e., the strength vs. the proportion of variance\nexplained and leads to different interpretations. It is essential to clearly state which metric was used and specify the type of\ncorrelation (i.e., Pearson or Spearman) depending on the data distribution and assumptions. Additionally, correlation plots\nshould include 95% confidence intervals, a fitted regression line and corrections for multiple comparisons.\n3. Figure 7: While it is understandable that the plots were grouped by clusters, reordering the x-axes to enable a consistent\norder across all panels would greatly improve readability and facilitate direct comparisons and interpretation across clusters.\n4. Figure 9: Could the authors clarify whether tumors had already formed prior to T cell injection and if so, what the tumor\nsizes were at the time of injection and following treatment? Additionally, the rationale for selecting Day 6 as the time point for\nT cell administration was not clearly explained and should be further justified.\n5. Figure 9: Based on the heatmaps in Figure 7, TIG.B.2 and TIG.G.1 showed very similar effector molecule expression\nprofiles and cluster closely in Figure 8D. However, their in vivo efficacy in the humanized mouse model (Figure 9) differed\nquite significantly. Could the authors comment on this apparent inconsistency? Might it suggest that the effector molecules\nused here were not sufficient to fully predict in vivo performance or could it be due to the limitations in the dataset or the way\nthe integrative analysis was performed? This highlights the potential value of developing a combinatorial\nscoring system that brings together multiple functional and molecular features. Such an approach can strengthen the predictive power of in vitro\nassessments and serve as a more reliable preclinical indicator of therapeutic potency.\nMinor comments:\n1. Figure 2B and 2C: It is recommended to replace “AUC” with “cytotoxic potential” on the graphs as AUC was used here as\na proxy for cytotoxic function.\n2. Figure 2C: The term “division index” was used in both the axes and the Methods section whereas “proliferation index”\nappeared in the main text and figure legend. This inconsistency was confusing and should be corrected by consistently\nusing one term throughout the manuscript.\n3. Figure 6 and 7: Addition of titles per panel would be very helpful.\n4. Typo errors: Several typos and missing characters were noted throughout the manuscript. Examples included:\na. Line 216: “TIG.1” was missing the letter “G.”\nb. Clone names: Inconsistent use of periods (e.g., missing “.” in clone identifiers).\nc. Line 227: The word “form” appeared to be a typo and should be corrected to “from”", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "In this manuscript, Smith and co-authors present a multidimensional analytical platform aimed at deeply characterizing the\nspecific features of various batches of Epstein-Barr virus (EBV)-specific T cells. The study includes comprehensive in vitro\nand in vivo analyses of antiviral activity, T-cell receptor (TCR) repertoire profiling, gene expression profiling, and\nimmunophenotyping. The overarching goal is to develop a novel approach for predicting the quality and therapeutic\npotential of allogeneic EBV-specific T cell products.\nWhile the extensive characterization is noteworthy and commendable, the manuscript suffers from limitations in data\ninterpretation and a lack of clarity regarding the biological and clinical implications of the results. In particular, the criteria\nused to define the most promising T cell batches remain ambiguous, and no assessment of safety—an essential parameter\nfor allogeneic T cell therapies—has been conducted. These gaps hinder a comprehensive understanding of the significance\nand translational value of the findings.\nSpecific Comments\n1. Figure 4A is unclear and needs to be better explained, either in the main text or figure legend.\n2. Lines 139–140: The authors suggest that CDR3 sharing between unrelated donors is likely due to shared HLA restriction\nand epitope specificity. This is an intriguing hypothesis but lacks robust data support. A detailed analysis demonstrating a\nhigh level of CDR3 sharing among T cells recognizing the same EBV epitope or restricted by the same HLA type should be\nprovided, accompanied by a clear graphical representation.\n3. Line 144: The claim that the QGG motif is associated with TCRs recognizing the LMP2 epitope should be substantiated.\nPlease cite supporting literature or provide evidence from the dataset.\n4. Lines 170–173: The authors should elaborate on the biological interpretation of the observed transcriptional or phenotypic\ndifferences. How do the distinct EBV-specific T cell batches differ in function or potential clinical relevance?\n5. Features related to in vivo persistence of the T cell products were not analyzed. Persistence is a critical parameter for\nevaluating and predicting the therapeutic efficacy of adoptive T cell therapies and should be discussed.\n6. The authors note a correlation between gene expression profiles and phenotypic data. However, the current\nrepresentation does not allow easy interpretation. A summarized tabular format integrating key findings from the different\nassays could significantly aid in understanding these correlations.\n7. Line 185: The stated clustering of TIG.A.1, TIG.B.1, TIG.B.2, TIG.F.1, and TIG.G.1 in both gene expression and phenotypic\nprofiles needs clarification. TIG.F.1 does not appear to cluster similarly in the phenotypic analysis, and TIG.H.1 is missing.\nPlease reconcile and explain this discrepancy.\n8. Figure 7: The authors claim a high degree of similarity in clustering across T cell batches derived by stimulation, cytokine\ncapture, or HLA-multimer enrichment. However, Fig. 7D suggests the presence of several metabolism-related genes that are\ndistinct in the multimer condition. This statement should be revised for accuracy and clarified.\n9. Figure 8: The biological and clinical interpretation of these findings should be clearly articulated. What do these data\nimply for T cell function and therapeutic potential?\n10. Please specify the biological characteristics of the EBV T cell cultures selected for in vivo validation. How were they\nchosen?\n11. The claim regarding CD4+ T cell reconstitution is overstated. This expansion is only evident in the spleen, and only\nTIG.J.2 demonstrates significant CD4+ expansion in other anatomical locations. Please correct this statement.\n12. A safety evaluation of the T cell products is missing. This is a critical component, especially in the context of allogeneic\ntherapies.\n13. A clearer conclusion should be provided regarding which parameters best predict clinical efficacy of EBV-specific T cell\nproducts. Ideally, safety assessment should also be considered.\n14. The lack of correlation between T cell proliferation and effector function is an important observation. The authors should\ndiscuss potential explanations.\n15. Figure 3A: Shouldn’t the overall CDR3 frequency be 100%? Please clarify whether this figure refers only to shared EBV-\nspecific TCRs between PBMC and product and how the frequency is calculated.\n16. Figure 4: The representation of shared CDR3 sequences is unclear. Why are some sequences apparently shared within\nthe same sample?\n17. Line 694: The title refers to TRBV diversity, but the figure actually addresses CDR3 diversity and sharing. Please correct\nthis. The term TRBV seems to be incorrectly used in place of “CDR3 sequences” in several instances (e.g., Line 132).\nPlease revise accordingly.\nIt is important to note that TRBV and CDR3 refer to different regions of the T cell receptor and cannot be used\ninterchangeably.\n18. Figure 6: The cluster at the center of the heatmap appears specifically enriched in the stimulated condition. The authors\nshould discuss the potential biological implications. Additional clusters that emerge only in specific conditions should also\nbe described.\n19. Figure 7A: Some T cell batches display a cytotoxic signature even in the unstimulated condition. Could the authors\ncomment on the potential origin or significance of this finding?\n20) Line 133: The claim of consistency in TCR diversity between replicate batches is not fully accurate. For example, TIG.D\nreplicates do not show clear consistency.\n21) Were the experiments performed only once? If so, this raises concerns. Technical replicates should be clearly stated.\n22) In the functional assays, inclusion of a negative control would strengthen the assessment of T cell efficacy. Please\nindicate if one was used.", "reviewer_4": "In their resource paper, Smith and colleagues describe a multi-omics platform to evaluate the phenotypic and functional\nproperties of allogeneic EBV-specific T cells, incorporating the analysis of T cell repertoires, transcriptomes, and flow\ncytometry. The overall goal is to establish “potency attributes” of EBV-specific T cell therapy batches for clinical studies.\nThe authors have established a repository of allogeneic EBV-specific T cell therapy products. To achieve broad HLA\ncoverage, 21 batches were generated from 12 donors recruited through a bone marrow program. The manuscript describes\nthe characterization of each of these batches using a variety of immunological assays, including degranulation, cytokine\nsecretion, proliferation, cytotoxicity, T cell repertoire analysis, and gene expression. The relationship between gene\nexpression profiles and other attributes of the EBV-specific T cell therapy batches, such as degranulation, proliferation and T\ncell repertoires was investigated using PCA-based approaches. Finally, 4 of the T cell therapy batches were tested in a\nxenogeneic EBV-LCL tumor model to assess their potency. This analysis revealed a strikingly discordant potency of the T\ncell therapy products to reduce EBV DNA load following adoptive transfer. mice. Unfortunately, the mechanisms underlying\nthis discordant activity are not further investigated or discussed.\nMain points:\n- This study appears to be well designed, and the data could be of interest. The main message this study would like to\nconvey is, however, not clear. The authors have analyzed many immunological parameters of the allogeneic EBV-specific T\ncell therapy batches, but it remains unclear which parameter(s) may be associated with potency to be successfully used in\nadoptive T cell therapy. Given the large difference in therapeutic outcomes observed in recent clinical trials, this information\nis of critical importance to move this approach through clinical studies.\n- The integration of the different parameters (Figure 8) should be the key aspect of this study, establishing potentially\nimportant correlations with the potency of the T cell therapy products. The results of this part are not clear and key\ninformation (scripts, explanation why the PCA approach was chosen) are not provided. Alternative approaches of data\nintegration should be tested. This part of the study needs to be improved.\n- The pre-clinical model (Figure 9) is of interest. Do the authors consider that the potency of the different EBV-specific T cell\ntherapy batches in the xenogeneic EBV-LCL tumor model may predict potency in patients? If so, why did they test only 4 of\nthe 21 batches and why did they not define which parameters analyzed in Figures 1-7 correlate with potency in the model?\nThere is obviously the caveat that this is a mouse model, however, it would provide a potentially important message and a\ngood basis for further studies.\nSpecific questions:\n- Figure 2A: data are represented in heat maps to give a condensed overview. Detailed data corresponding to the individual\nmeasurements of degranulation and cytokine production should be provided as a supplementary figure or in a\nsupplementary data file.\n- Figures 3 and 4: the analysis has been restricted to TRBV sequences using the ImmunoSEQ platform. A clonal analysis\nincluding the analysis of TRAV sequences was not performed. What was the reason for this choice?\n- Figure 6: have the correlations been calculated on the average of all 326 genes or differentially expressed genes between\ntherapy batches? In any case, all gene expression data need to be included in a supplementary data file or made publicly\navailable in a database.\n- Figure 7: according to the methods section, differentially expressed genes were calculated between unstimulated and\nstimulated as well as between cell isolated by cytokine capture and sorting using multimers and top 20 genes were\nrepresented. The top 20 genes of which contrast are shown? The figure legend is not helpful.\n- Figure 8: integration of the different parameters analyzed in this study is a key part of this study, but it is not clear how this\nanalysis was performed. The results section refers to the Methods but no paragraph can be found in the methods section. A\ndetailed description of this analysis is essential, and scripts should be provided.\nMinor points:\nFig. 1B: x- and y-axes labels are not readable. Please enlarge or explain in figure legend. NP = no peptide?\nThe text would benefit from proofreading."}, "author_response": "We would like thank all four reviewers for their helpful comments and suggestion. We have tried to\naddress all their concerns and our response to each of their comments are provided below.\nResponse to Reviewers #1 & #2\nSummary:\nIn this study, the authors presented a multidimensional analytical platform to characterize allogeneic\ndonor-derived EBV-specific T cell batches, with the goal of identifying attributes linked to therapeutic\npotency. They reported consistent enrichment of potent effector-like T cells across donors though\nsome variability in proliferative and cytolytic capacity appeared to be donor-intrinsic. However, the\nauthors would need to establish a definitive prediction score derived from the multidimensional\nanalytical platform with in vivo assay in order to provide convincing evidence of its utility in the\ncharacterisation of allogeneic EBV-specific T cell therapies.\nWe thank the reviewers for their comment and agree that the final objective of the current approach\ncould be to generate a predictive score that would allow you to predict whether the T cells are likely to\nhave therapeutic benefit. We have created an initial predictive score/model for our T cell products that\nis based upon all of the assays used in the current study. In this approach we provide a simple 1-3 or\n1-2 score for each of the parameters tested (please see Supplemental Table 6). In addition, we have\nalso included a new figure 8 summarising this scoring matrix for T cell products manufactured from\ndifferent starting material. We have added this information in the revised text (see lines 208-218).\nMajor comments on data, methodology and analytical approach, data interpretation and conclusion:\n1. Figure 2A: This panel showed the proportion of T cells expressing specific markers. We assume the\ndata were presented as raw proportions and not scaled. To better highlight the polyfunctional\ncharacteristics across therapy batches, it might be more informative to scale the values per marker\nacross products within each HLA restriction group. This normalization would make it easier to compare\nrelative marker expression and functional diversity across different batches.\nThis data is already scaled and normalised to total antigen-specific cells detected in each product (Fig.\n2A). As requested by another reviewer, we have now provided detailed raw data from these analyses\nas Supplemental Table 1.\n2. Figure 2 and 8: It is unclear whether the correlation analyses were based on the correlation\ncoefficient (r) or the coefficient of determination (r²), as the methods provided limited detail. While\nthe figure labels suggested r², the context implied that r was used. This distinction is critical as r and\nr² convey different meanings, i.e., the strength vs. the proportion of variance explained and leads to\ndifferent interpretations. It is essential to clearly state which metric was used and specify the type of\ncorrelation (i.e., Pearson or Spearman) depending on the data distribution and assumptions.\nAdditionally, correlation plots should include 95% confidence intervals, a fitted regression line and\ncorrections for multiple comparisons.\nPlease note the data presented in figure 2 is based on simple linear regression using GraphPad Prism\nsoftware and used Pearson to calculate the coefficient of determination (r2). We have added this\ninformation in the revised text (see lines 409-411). As requested by the reviewer, we have also included\n95% confidence intervals, a fitted regression line and corrections for multiple comparisons (see revised\nFigure 2). For data presented in Figure 8 (now Figure 9), Principal Component Analysis was used to\ncluster the TIG products based on their Nanostring gene expression profiles. Log2-normalized gene\nexpression profiles were used to derive the top two PCA components. The PCA plot shows the relative\nplacement of the TIG products on a two-dimensional plane representing the two PCA components and\nthe key genes that contribute to the components. Product features namely, proliferation, CD107a, and\nTCR diversities, were overlaid on the TIG products on the PCA plot. We have added this information in\nthe revised text (see lines 514-520).\n3. Figure 7: While it is understandable that the plots were grouped by clusters, reordering the x-axes\nto enable a consistent order across all panels would greatly improve readability and facilitate direct\ncomparisons and interpretation across clusters.\nThese plots are grouped via unsupervised clustering. This provides the most efficient way to group the\nT cell products according to their gene signature and also provides an opportunity to compare\nunsupervised clustering in the different conditions. We believe this is the most appropriate way to\ndisplay this data. As a consequence we have not revised the order of these samples\n4. Figure 9: Could the authors clarify whether tumors had already formed prior to T cell injection and\nif so, what the tumor sizes were at the time of injection and following treatment? Additionally, the\nrationale for selecting Day 6 as the time point for T cell administration was not clearly explained and\nshould be further justified.\nThe rationale for treating mice with T cells on day 6 was that measurable tumour volumes could be\nobserved. Tumours were measured, and mice were randomized into different groups to ensure equal\ntumour volumes before T-cell injections. Tumours were measured at regular intervals. Although T-cell\ntreatment resulted in a reduction in tumour volume, LCLs were able to disseminate into other organs\ndepending on the quality of the T cells. To appropriately assess the extent of T-cell–mediated control\nof LCL outgrowth, viral loads were measured in different organs (Fig. 10B–D). We have revised the text\nto include this information (see lines 525-528).\n5. Figure 9: Based on the heatmaps in Figure 7, TIG.B.2 and TIG.G.1 showed very similar effector\nmolecule expression profiles and cluster closely in Figure 8D. However, their in vivo efficacy in the\nhumanized mouse model (Figure 9) differed quite significantly. Could the authors comment on this\napparent inconsistency? Might it suggest that the effector molecules used here were not sufficient to\nfully predict in vivo performance or could it be due to the limitations in the dataset or the way the\nintegrative analysis was performed? This highlights the potential value of developing a combinatorial\nscoring system that brings together multiple functional and molecular features. Such an approach can\nstrengthen the predictive power of in vitro assessments and serve as a more reliable preclinical\nindicator of therapeutic potency.\nWe agree with the reviewer that factors other than cytotoxicity are likely critical to the potency of our\ncell therapy. This was the rationale behind adopting the approach we have undertaken in the study\nthat provides a more comprehensive analysis of cell function. We do show in the PCA plots provided in\nFigure 8 (now Figure 9), that the TIG.G.1 product has a unique signature compared to the TIG.B\nproducts, despite the comparable cytotoxic gene profile. We have now highlighted this more clearly in\nthe results section (see lines 222-237).\nMinor comments:\n1. Figure 2B and 2C: It is recommended to replace “AUC” with “cytotoxic potential” on the graphs as\nAUC was used here as a proxy for cytotoxic function.\nAs requested by the reviewers, we have now replaced AUC with cytotoxic potential.\n2. Figure 2C: The term “division index” was used in both the axes and the Methods section\nwhereas “proliferation index” appeared in the main text and figure legend. This inconsistency\nwas confusing and should be corrected by consistently using one term throughout the\nmanuscript.\nAs requested by the reviewers, we have replaced proliferation index with division index.\n3. Figure 6 and 7: Addition of titles per panel would be very helpful.\nAs requested by the reviewers, we have now added titles to Figures 6 and 7\n4. Typo errors: Several typos and missing characters were noted throughout the manuscript. Examples\nincluded:\na. Line 216: “TIG.1” was missing the letter “G.”\nWe have corrected the text as recommended by the reviewers. We apologies for this error.\nb. Clone names: Inconsistent use of periods (e.g., missing “.” in clone identifiers).\nWe have corrected this error.\nc. Line 227: The word “form” appeared to be a typo and should be corrected to “from”\nWe have corrected this error.\nResponse to Reviewer #3\nIn this manuscript, Smith and co-authors present a multidimensional analytical platform aimed at\ndeeply characterizing the specific features of various batches of Epstein-Barr virus (EBV)-specific T\ncells. The study includes comprehensive in vitro and in vivo analyses of antiviral activity, T-cell receptor\n(TCR) repertoire profiling, gene expression profiling, and immunophenotyping. The overarching goal\nis to develop a novel approach for predicting the quality and therapeutic potential of allogeneic EBV-\nspecific T cell products.\nWhile the extensive characterization is noteworthy and commendable, the manuscript suffers from\nlimitations in data interpretation and a lack of clarity regarding the biological and clinical implications\nof the results. In particular, the criteria used to define the most promising T cell batches remain\nambiguous, and no assessment of safety—an essential parameter for allogeneic T cell therapies—has\nbeen conducted. These gaps hinder a comprehensive understanding of the significance and\ntranslational value of the findings.\nWe appreciate the reviewer’s comment. As part of our routine cell therapy manufacturing we perform\na standard assessment of alloreactivity. All products were below our threshold for alloreactivity (<1%\nof CD107a+ CD3+ T cells in response to HLA-mismatched targets). In our animal monitoring, we\nperformed assessment for safety that include measures for evidence of GVHD. We saw no evidence of\nGVHD or other safety concerns related to the use of the T cell products. We have now commented on\nthese results in the revised text (see lines 247-248).\nSpecific Comments\n1. Figure 4A is unclear and needs to be better explained, either in the main text or figure legend.\nAs requested by the reviewer, we have now added more detail into the results sections (see lines 129-\n135). Please note detailed information is also provided in the figure legend (see Figure 4 legend).\n2. Lines 139–140: The authors suggest that CDR3 sharing between unrelated donors is likely due to\nshared HLA restriction and epitope specificity. This is an intriguing hypothesis but lacks robust data\nsupport. A detailed analysis demonstrating a high level of CDR3 sharing among T cells recognizing the\nsame EBV epitope or restricted by the same HLA type should be provided, accompanied by a clear\ngraphical representation.\nPlease note that the QGG motif for the FLY epitope is already published. We have now added a\nreference reporting this data. Public sharing of TCR between individuals is a well-established\nphenomenon and has been reported in many studies. We therefore do not believe the study would be\nenhanced by further confirming the CDR3 sharing by isolating epitope specific T cells and performing\nfurther CDR3 analysis. We have now added the reference for the LMP2 public TCR.\n3. Line 144: The claim that the QGG motif is associated with TCRs recognizing the LMP2 epitope should\nbe substantiated. Please cite supporting literature or provide evidence from the dataset.\nWe have now added the reference for the LMP2 public TCR. “Public T-Cell Receptors (TCRs) Revisited\nby Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires\nof Healthy Individuals”(see ref 29).\n4. Lines 170–173: The authors should elaborate on the biological interpretation of the observed\ntranscriptional or phenotypic differences. How do the distinct EBV-specific T cell batches differ in\nfunction or potential clinical relevance?\nWe have now revised teh text in the results section describing the link between the transcriptional and\nphenotypic differences in our T cells (see lines 185-196 and 197-218). We have also added more\ndiscussion on the potential clinical significance of these results (see lines 343-357).\n5. Features related to in vivo persistence of the T cell products were not analyzed. Persistence is a\ncritical parameter for evaluating and predicting the therapeutic efficacy of adoptive T cell therapies\nand should be discussed.\nThe persistence of T-cell numbers and their functionality was measured and is presented in Figure 10E–\nG. We have now added some discussion on this persistence particularly the lack of persistence for\nTIG.G.1 (see lines 343-357).\n6. The authors note a correlation between gene expression profiles and phenotypic data. However,\nthe current representation does not allow easy interpretation. A summarized tabular format\nintegrating key findings from the different assays could significantly aid in understanding these\ncorrelations.\nWe would like to thank the reviewer for an excellent suggestion. As recommended by the reviewer we\nhave now added a new Supplemental Table 6 and Figure 8 that provides a detailed scoring matrix for\nall of the findings from each of the assays in the study. We have summarised each assay based on the\nfunctional and TCR analysis relative to the other T cell products. For the phenotypic analysis we have\nsummarised the results based on whether the MHC-Multimer+ cells have a bias towards a central\nmemory type phenotype (cluster 1 with higher CD28 expression) or an effector memory phenotype\n(cluster 4 with higher CD57 expression). For gene signatures we have separated the products based\nupon the expression of effector genes.\n7. Line 185: The stated clustering of TIG.A.1, TIG.B.1, TIG.B.2, TIG.F.1, and TIG.G.1 in both gene\nexpression and phenotypic profiles needs clarification. TIG.F.1 does not appear to cluster similarly in\nthe phenotypic analysis, and TIG.H.1 is missing. Please reconcile and explain this discrepancy.\nWe thank the reviewer for their comment. This was an error or our behalf. This should have TIG.H.1\ninstead of TIG.F.1.\n8. Figure 7: The authors claim a high degree of similarity in clustering across T cell batches derived by\nstimulation, cytokine capture, or HLA-multimer enrichment. However, Fig. 7D suggests the presence of\nseveral metabolism-related genes that are distinct in the multimer condition. This statement should\nbe revised for accuracy and clarified.\nWe thank the reviewer for this excellent observations. We also note the increased expression of\nmetabolism-related genes (ALDOA, PGAM1, LDHB and TPI1) in the MHC-Multimer sorted T cells and\nthe Unstimulated T cells. We now revised the text to include this information (see lines 198-218).\n9. Figure 8: The biological and clinical interpretation of these findings should be clearly articulated.\nWhat do these data imply for T cell function and therapeutic potential?\nWe have updated our concluding remarks to provide some interpretation around the impact of our\nfindings may have upon the therapeutic potential of EBV-specific T cells (see lines 360-365).\n10. Please specify the biological characteristics of the EBV T cell cultures selected for in vivo validation.\nHow were they chosen?\nThe EBV-specific T cells were selected based upon the availability of material. We also ensured they\nwere selected to represent products that had differential clustering in Figure 9.\n11. The claim regarding CD4+ T cell reconstitution is overstated. This expansion is only evident in the\nspleen, and only TIG.J.2 demonstrates significant CD4+ expansion in other anatomical locations.\nPlease correct this statement.\nWe thank the reviewer for this comment and have corrected this statement in the text.\n12. A safety evaluation of the T cell products is missing. This is a critical component, especially in the\ncontext of allogeneic therapies.\nAs part of our routine cell therapy manufacturing we perform a standard assessment of alloreactivity.\nAll products were below our threshold for alloreactivity (<1% of CD107a+ CD3+ T cells in response to\nHLA-mismatched targets). In our animal monitoring we perform assessment for safety that include\nmeasures for evidence of GVHD. We saw no evidence of GVHD or other safety concerns related to the\nuse of the T cell products. We have now commented on these results in the text (see lines 247-248).\n13. A clearer conclusion should be provided regarding which parameters best predict clinical efficacy\nof EBV-specific T cell products. Ideally, safety assessment should also be considered.\nWe have updated our conclusion to provide more nuance in parameters that may predict clinical\nefficacy. We have not included safety in this conclusion because we saw no evidence of adverse events\nin our animal study, which is consistent with the already well known safety profile of allogeneic EBV-\nspecific T cells in humans (see lines 360-365).\n14. The lack of correlation between T cell proliferation and effector function is an important\nobservation. The authors should discuss potential explanations.\nWe agree that this is an interesting observation. However this requires further investigation before we\nare able to discuss any potential explanations.\n15. Figure 3A: Shouldn’t the overall CDR3 frequency be 100%? Please clarify whether this figure refers\nonly to shared EBV-specific TCRs between PBMC and product and how the frequency is calculated.\nThe products contain some T cells that are not EBV-specific. The only TCR included in the Fig 3A are\nthose defined as EBV-specific following cytokine capture.\n16. Figure 4: The representation of shared CDR3 sequences is unclear. Why are some sequences\napparently shared within the same sample?\nWe thank the reviewer for this observation. We have now corrected this figure to only show sharing\nbetween different products (see Figure 4).\n17. Line 694: The title refers to TRBV diversity, but the figure actually addresses CDR3 diversity and\nsharing. Please correct this. The term TRBV seems to be incorrectly used in place of “CDR3 sequences”\nin several instances (e.g., Line 132). Please revise accordingly. It is important to note that TRBV and\nCDR3 refer to different regions of the T cell receptor and cannot be used interchangeably.\nThis has now been corrected throughout the manuscript.\n18. Figure 6: The cluster at the center of the heatmap appears specifically enriched in the stimulated\ncondition. The authors should discuss the potential biological implications. Additional clusters that\nemerge only in specific conditions should also be described.\nWe have now addressed this issue in the manuscript.\n19. Figure 7A: Some T cell batches display a cytotoxic signature even in the unstimulated condition.\nCould the authors comment on the potential origin or significance of this finding?\nCytotoxic signatures are commonly expressed and maintained in human herpesvirus (EBV/CMV)-\nspecific T cells at rest. This is common in resting T cell populations assessed directly ex vivo in human\nPBMC.\n20) Line 133: The claim of consistency in TCR diversity between replicate batches is not fully accurate.\nFor example, TIG.D replicates do not show clear consistency.\nWe thank the reviewer for their comment. We have now removed this statement from the text.\n21) Were the experiments performed only once? If so, this raises concerns. Technical replicates should\nbe clearly stated.\nWe thank the reviewer for their comment and have added details to the text for experiments that were\nperformed with replicates.\n22) In the functional assays, inclusion of a negative control would strengthen the assessment of T cell\nefficacy. Please indicate if one was used.\nYes all assays use a negative control. These are subtracted from the test values to provide a final result\nvalue.\nResponse to Reviewer #4\nIn their resource paper, Smith and colleagues describe a multi-omics platform to evaluate the\nphenotypic and functional properties of allogeneic EBV-specific T cells, incorporating the analysis of T\ncell repertoires, transcriptomes, and flow cytometry. The overall goal is to establish “potency\nattributes” of EBV-specific T cell therapy batches for clinical studies.\nThe authors have established a repository of allogeneic EBV-specific T cell therapy products. To\nachieve broad HLA coverage, 21 batches were generated from 12 donors recruited through a bone\nmarrow program. The manuscript describes the characterization of each of these batches using a\nvariety of immunological assays, including degranulation, cytokine secretion, proliferation,\ncytotoxicity, T cell repertoire analysis, and gene expression. The relationship between gene\nexpression profiles and other attributes of the EBV-specific T cell therapy batches, such as degranulation,\nproliferation and T cell repertoires was investigated using PCA-based approaches. Finally, 4 of the T\ncell therapy batches were tested in a xenogeneic EBV-LCL tumor model to assess their potency. This\nanalysis revealed a strikingly discordant potency of the T cell therapy products to reduce EBV DNA\nload following adoptive transfer. mice. Unfortunately, the mechanisms underlying this discordant\nactivity are not further investigated or discussed.\nWe appreciate the reviewer’s comment on the mechanisms underlying this discordant activity and\nhave addressed this in the revised version of the results and discussion section (see below).\nMain points:\n1. This study appears to be well designed, and the data could be of interest. The main message\nthis study would like to convey is, however, not clear. The authors have analyzed many immunological\nparameters of the allogeneic EBV-specific T cell therapy batches, but it remains unclear which\nparameter(s) may be associated with potency to be successfully used in adoptive T cell therapy. Given\nthe large difference in therapeutic outcomes observed in recent clinical trials, this information is of\ncritical importance to move this approach through clinical studies.\nWe agree with the reviewer and have now revised the text especially the results and discussion section\nto highlight/identify parameter(s) may be associated with potency to be successfully used in adoptive\nT cell therapy (see lines 222-237 and 343-357).\n2. The integration of the different parameters (Figure 8) should be the key aspect of this study,\nestablishing potentially important correlations with the potency of the T cell therapy products. The\nresults of this part are not clear and key information (scripts, explanation why the PCA approach was\nchosen) are not provided. Alternative approaches of data integration should be tested. This part of\nthe study needs to be improved.\nWe would like to thank the reviewer for her/his advice and have tried to address the limitations\nidentified. We have revised the manuscript text (methods, results and discussion) to integrate different\nparameters (See new Supplemental Table 6 and Figure 8).\n3. The pre-clinical model (Figure 9) is of interest. Do the authors consider that the potency of the\ndifferent EBV-specific T cell therapy batches in the xenogeneic EBV-LCL tumor model may predict\npotency in patients? If so, why did they test only 4 of the 21 batches and why did they not define\nwhich parameters analyzed in Figures 1-7 correlate with potency in the model? There is obviously the\ncaveat that this is a mouse model, however, it would provide a potentially important message and a\ngood basis for further studies.\nYes, we do see that the xenogeneic EBV-LCL tumor model may predict potency in patients. However,\nthe use of broader functional and transcriptional framework outlined in this manuscript will be\nrequired to strengthen this predictability. We did use the parameters analysed in figures 1-7 to select\nfour batches for in vivo studies. We have now revised the results and discussion section to outline this\nin more detail (see lines 239-245).\nSpecific questions:\n4. Figure 2A: data are represented in heat maps to give a condensed overview. Detailed data\ncorresponding to the individual measurements of degranulation and cytokine production should be\nprovided as a supplementary figure or in a supplementary data file.\nAs requested by the reviewer, we have added all corresponding data for Fig. 2A as supplemental data\nfile.\n5. Figures 3 and 4: the analysis has been restricted to TRBV sequences using the ImmunoSEQ platform.\nA clonal analysis including the analysis of TRAV sequences was not performed. What was the reason\nfor this choice?\nAt the time of data generation we decided that the TRBV deep sequencing analysis would provide us\nwill ample information on TCR usage in our products, rather than using a single cell approach to study\nboth TRBV and TRAV.\n6. Figure 6: have the correlations been calculated on the average of all 326 genes or differentially\nexpressed genes between therapy batches? In any case, all gene expression data need to be included\nin a supplementary data file or made publicly available in a database.\nAs requested by the reviewer, we have added all gene expression data as supplemental data file.\n7. Figure 7: according to the methods section, differentially expressed genes were calculated between\nunstimulated and stimulated as well as between cell isolated by cytokine capture and sorting using\nmultimers and top 20 genes were represented. The top 20 genes of which contrast are shown? The\nfigure legend is not helpful.\nWe apologise for this confusion. Differentially expressed genes were determined between samples\nwithin each parameter following house-keeping normalised. We did not compare between stimulated\nand unstimulated. We have corrected the methods section to reflect this. We have updated figure\nlegend of Figure 7 stating that the top 20 differentially expressed genes for each panel is shown.\n8. Figure 8: integration of the different parameters analyzed in this study is a key part of this study,\nbut it is not clear how this analysis was performed. The results section refers to the Methods but no\nparagraph can be found in the methods section. A detailed description of this analysis is essential, and\nscripts should be provided.\nWe have revised the methods and results section to provide more details on data presented in Figure\n8 .\nMinor points:\nFig. 1B: x- and y-axes labels are not readable. Please enlarge or explain in figure legend. NP = no\npeptide?\nWe have updated Figure 1B as recommended by the reviewer.\nThe text would benefit from proofreading.\nWe would like to thank the reviewer and have carefully revised the text.\nReplies to reviewers comments\nWe would like to express our sincere thanks to all four reviewers for their helpful suggestions and\nrecommendations. These comments have been immensely valuable for us to improve our manuscript.\nReviewer #3 (Remarks to the Author):\nAuthors provided appropriate responses / clarifications to my previous comments.\nThe only remaining concern is related to the functional assays. The authors state that in all assays\nnegative controls were used and were subtracted from the test values to provide a final result value.\nHonestly, I would prefer to see them represented separately and not subtracted from the positive\nvalues.\nWe appreciate reviewer’s comment. We respectfully disagree and prefer to keep figures as its. We have\nprovided all raw data including negative controls in the supplemental source data.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed the points I have raised during the first round of reviewing.\nMinor points:\n- Supplementary Table 5 is not included in the supplementary data file\n- Despite my request, x-axis labels have not been added to Fig. 1B\nSupplemental Table 5 was already included in the supplemental files. We have now added this in the\nsupplemental material file.\nWe apologise for the oversight on x-axis labels in Figure 1B. We have now added these labels in the\nfigure."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all our comments and suggestions and have included the predictive score of the T\ncell product.\nWe have no additional comments on the revised manuscript.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Authors provided appropriate responses / clarifications to my previous comments.\nThe only remaining concern is related to the functional assays. The authors state that in all assays negative controls were\nused and were subtracted from the test values to provide a final result value. Honestly, I would prefer to see them\nrepresented separately and not subtracted from the positive values.", "reviewer_4": "The authors have addressed the points I have raised during the first round of reviewing.\nMinor points:\n- Supplementary Table 5 is not included in the supplementary data file\n- Despite my request, x-axis labels have not been added to Fig. 1B"}, "author_response": "We would like thank all four reviewers for their helpful comments and suggestion. We have tried to\naddress all their concerns and our response to each of their comments are provided below.\nResponse to Reviewers #1 & #2\nSummary:\nIn this study, the authors presented a multidimensional analytical platform to characterize allogeneic\ndonor-derived EBV-specific T cell batches, with the goal of identifying attributes linked to therapeutic\npotency. They reported consistent enrichment of potent effector-like T cells across donors though\nsome variability in proliferative and cytolytic capacity appeared to be donor-intrinsic. However, the\nauthors would need to establish a definitive prediction score derived from the multidimensional\nanalytical platform with in vivo assay in order to provide convincing evidence of its utility in the\ncharacterisation of allogeneic EBV-specific T cell therapies.\nWe thank the reviewers for their comment and agree that the final objective of the current approach\ncould be to generate a predictive score that would allow you to predict whether the T cells are likely to\nhave therapeutic benefit. We have created an initial predictive score/model for our T cell products that\nis based upon all of the assays used in the current study. In this approach we provide a simple 1-3 or\n1-2 score for each of the parameters tested (please see Supplemental Table 6). In addition, we have\nalso included a new figure 8 summarising this scoring matrix for T cell products manufactured from\ndifferent starting material. We have added this information in the revised text (see lines 208-218).\nMajor comments on data, methodology and analytical approach, data interpretation and conclusion:\n1. Figure 2A: This panel showed the proportion of T cells expressing specific markers. We assume the\ndata were presented as raw proportions and not scaled. To better highlight the polyfunctional\ncharacteristics across therapy batches, it might be more informative to scale the values per marker\nacross products within each HLA restriction group. This normalization would make it easier to compare\nrelative marker expression and functional diversity across different batches.\nThis data is already scaled and normalised to total antigen-specific cells detected in each product (Fig.\n2A). As requested by another reviewer, we have now provided detailed raw data from these analyses\nas Supplemental Table 1.\n2. Figure 2 and 8: It is unclear whether the correlation analyses were based on the correlation\ncoefficient (r) or the coefficient of determination (r²), as the methods provided limited detail. While\nthe figure labels suggested r², the context implied that r was used. This distinction is critical as r and\nr² convey different meanings, i.e., the strength vs. the proportion of variance explained and leads to\ndifferent interpretations. It is essential to clearly state which metric was used and specify the type of\ncorrelation (i.e., Pearson or Spearman) depending on the data distribution and assumptions.\nAdditionally, correlation plots should include 95% confidence intervals, a fitted regression line and\ncorrections for multiple comparisons.\nPlease note the data presented in figure 2 is based on simple linear regression using GraphPad Prism\nsoftware and used Pearson to calculate the coefficient of determination (r2). We have added this\ninformation in the revised text (see lines 409-411). As requested by the reviewer, we have also included\n95% confidence intervals, a fitted regression line and corrections for multiple comparisons (see revised\nFigure 2). For data presented in Figure 8 (now Figure 9), Principal Component Analysis was used to\ncluster the TIG products based on their Nanostring gene expression profiles. Log2-normalized gene\nexpression profiles were used to derive the top two PCA components. The PCA plot shows the relative\nplacement of the TIG products on a two-dimensional plane representing the two PCA components and\nthe key genes that contribute to the components. Product features namely, proliferation, CD107a, and\nTCR diversities, were overlaid on the TIG products on the PCA plot. We have added this information in\nthe revised text (see lines 514-520).\n3. Figure 7: While it is understandable that the plots were grouped by clusters, reordering the x-axes\nto enable a consistent order across all panels would greatly improve readability and facilitate direct\ncomparisons and interpretation across clusters.\nThese plots are grouped via unsupervised clustering. This provides the most efficient way to group the\nT cell products according to their gene signature and also provides an opportunity to compare\nunsupervised clustering in the different conditions. We believe this is the most appropriate way to\ndisplay this data. As a consequence we have not revised the order of these samples\n4. Figure 9: Could the authors clarify whether tumors had already formed prior to T cell injection and\nif so, what the tumor sizes were at the time of injection and following treatment? Additionally, the\nrationale for selecting Day 6 as the time point for T cell administration was not clearly explained and\nshould be further justified.\nThe rationale for treating mice with T cells on day 6 was that measurable tumour volumes could be\nobserved. Tumours were measured, and mice were randomized into different groups to ensure equal\ntumour volumes before T-cell injections. Tumours were measured at regular intervals. Although T-cell\ntreatment resulted in a reduction in tumour volume, LCLs were able to disseminate into other organs\ndepending on the quality of the T cells. To appropriately assess the extent of T-cell–mediated control\nof LCL outgrowth, viral loads were measured in different organs (Fig. 10B–D). We have revised the text\nto include this information (see lines 525-528).\n5. Figure 9: Based on the heatmaps in Figure 7, TIG.B.2 and TIG.G.1 showed very similar effector\nmolecule expression profiles and cluster closely in Figure 8D. However, their in vivo efficacy in the\nhumanized mouse model (Figure 9) differed quite significantly. Could the authors comment on this\napparent inconsistency? Might it suggest that the effector molecules used here were not sufficient to\nfully predict in vivo performance or could it be due to the limitations in the dataset or the way the\nintegrative analysis was performed? This highlights the potential value of developing a combinatorial\nscoring system that brings together multiple functional and molecular features. Such an approach can\nstrengthen the predictive power of in vitro assessments and serve as a more reliable preclinical\nindicator of therapeutic potency.\nWe agree with the reviewer that factors other than cytotoxicity are likely critical to the potency of our\ncell therapy. This was the rationale behind adopting the approach we have undertaken in the study\nthat provides a more comprehensive analysis of cell function. We do show in the PCA plots provided in\nFigure 8 (now Figure 9), that the TIG.G.1 product has a unique signature compared to the TIG.B\nproducts, despite the comparable cytotoxic gene profile. We have now highlighted this more clearly in\nthe results section (see lines 222-237).\nMinor comments:\n1. Figure 2B and 2C: It is recommended to replace “AUC” with “cytotoxic potential” on the graphs as\nAUC was used here as a proxy for cytotoxic function.\nAs requested by the reviewers, we have now replaced AUC with cytotoxic potential.\n2. Figure 2C: The term “division index” was used in both the axes and the Methods section\nwhereas “proliferation index” appeared in the main text and figure legend. This inconsistency\nwas confusing and should be corrected by consistently using one term throughout the\nmanuscript.\nAs requested by the reviewers, we have replaced proliferation index with division index.\n3. Figure 6 and 7: Addition of titles per panel would be very helpful.\nAs requested by the reviewers, we have now added titles to Figures 6 and 7\n4. Typo errors: Several typos and missing characters were noted throughout the manuscript. Examples\nincluded:\na. Line 216: “TIG.1” was missing the letter “G.”\nWe have corrected the text as recommended by the reviewers. We apologies for this error.\nb. Clone names: Inconsistent use of periods (e.g., missing “.” in clone identifiers).\nWe have corrected this error.\nc. Line 227: The word “form” appeared to be a typo and should be corrected to “from”\nWe have corrected this error.\nResponse to Reviewer #3\nIn this manuscript, Smith and co-authors present a multidimensional analytical platform aimed at\ndeeply characterizing the specific features of various batches of Epstein-Barr virus (EBV)-specific T\ncells. The study includes comprehensive in vitro and in vivo analyses of antiviral activity, T-cell receptor\n(TCR) repertoire profiling, gene expression profiling, and immunophenotyping. The overarching goal\nis to develop a novel approach for predicting the quality and therapeutic potential of allogeneic EBV-\nspecific T cell products.\nWhile the extensive characterization is noteworthy and commendable, the manuscript suffers from\nlimitations in data interpretation and a lack of clarity regarding the biological and clinical implications\nof the results. In particular, the criteria used to define the most promising T cell batches remain\nambiguous, and no assessment of safety—an essential parameter for allogeneic T cell therapies—has\nbeen conducted. These gaps hinder a comprehensive understanding of the significance and\ntranslational value of the findings.\nWe appreciate the reviewer’s comment. As part of our routine cell therapy manufacturing we perform\na standard assessment of alloreactivity. All products were below our threshold for alloreactivity (<1%\nof CD107a+ CD3+ T cells in response to HLA-mismatched targets). In our animal monitoring, we\nperformed assessment for safety that include measures for evidence of GVHD. We saw no evidence of\nGVHD or other safety concerns related to the use of the T cell products. We have now commented on\nthese results in the revised text (see lines 247-248).\nSpecific Comments\n1. Figure 4A is unclear and needs to be better explained, either in the main text or figure legend.\nAs requested by the reviewer, we have now added more detail into the results sections (see lines 129-\n135). Please note detailed information is also provided in the figure legend (see Figure 4 legend).\n2. Lines 139–140: The authors suggest that CDR3 sharing between unrelated donors is likely due to\nshared HLA restriction and epitope specificity. This is an intriguing hypothesis but lacks robust data\nsupport. A detailed analysis demonstrating a high level of CDR3 sharing among T cells recognizing the\nsame EBV epitope or restricted by the same HLA type should be provided, accompanied by a clear\ngraphical representation.\nPlease note that the QGG motif for the FLY epitope is already published. We have now added a\nreference reporting this data. Public sharing of TCR between individuals is a well-established\nphenomenon and has been reported in many studies. We therefore do not believe the study would be\nenhanced by further confirming the CDR3 sharing by isolating epitope specific T cells and performing\nfurther CDR3 analysis. We have now added the reference for the LMP2 public TCR.\n3. Line 144: The claim that the QGG motif is associated with TCRs recognizing the LMP2 epitope should\nbe substantiated. Please cite supporting literature or provide evidence from the dataset.\nWe have now added the reference for the LMP2 public TCR. “Public T-Cell Receptors (TCRs) Revisited\nby Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires\nof Healthy Individuals”(see ref 29).\n4. Lines 170–173: The authors should elaborate on the biological interpretation of the observed\ntranscriptional or phenotypic differences. How do the distinct EBV-specific T cell batches differ in\nfunction or potential clinical relevance?\nWe have now revised teh text in the results section describing the link between the transcriptional and\nphenotypic differences in our T cells (see lines 185-196 and 197-218). We have also added more\ndiscussion on the potential clinical significance of these results (see lines 343-357).\n5. Features related to in vivo persistence of the T cell products were not analyzed. Persistence is a\ncritical parameter for evaluating and predicting the therapeutic efficacy of adoptive T cell therapies\nand should be discussed.\nThe persistence of T-cell numbers and their functionality was measured and is presented in Figure 10E–\nG. We have now added some discussion on this persistence particularly the lack of persistence for\nTIG.G.1 (see lines 343-357).\n6. The authors note a correlation between gene expression profiles and phenotypic data. However,\nthe current representation does not allow easy interpretation. A summarized tabular format\nintegrating key findings from the different assays could significantly aid in understanding these\ncorrelations.\nWe would like to thank the reviewer for an excellent suggestion. As recommended by the reviewer we\nhave now added a new Supplemental Table 6 and Figure 8 that provides a detailed scoring matrix for\nall of the findings from each of the assays in the study. We have summarised each assay based on the\nfunctional and TCR analysis relative to the other T cell products. For the phenotypic analysis we have\nsummarised the results based on whether the MHC-Multimer+ cells have a bias towards a central\nmemory type phenotype (cluster 1 with higher CD28 expression) or an effector memory phenotype\n(cluster 4 with higher CD57 expression). For gene signatures we have separated the products based\nupon the expression of effector genes.\n7. Line 185: The stated clustering of TIG.A.1, TIG.B.1, TIG.B.2, TIG.F.1, and TIG.G.1 in both gene\nexpression and phenotypic profiles needs clarification. TIG.F.1 does not appear to cluster similarly in\nthe phenotypic analysis, and TIG.H.1 is missing. Please reconcile and explain this discrepancy.\nWe thank the reviewer for their comment. This was an error or our behalf. This should have TIG.H.1\ninstead of TIG.F.1.\n8. Figure 7: The authors claim a high degree of similarity in clustering across T cell batches derived by\nstimulation, cytokine capture, or HLA-multimer enrichment. However, Fig. 7D suggests the presence of\nseveral metabolism-related genes that are distinct in the multimer condition. This statement should\nbe revised for accuracy and clarified.\nWe thank the reviewer for this excellent observations. We also note the increased expression of\nmetabolism-related genes (ALDOA, PGAM1, LDHB and TPI1) in the MHC-Multimer sorted T cells and\nthe Unstimulated T cells. We now revised the text to include this information (see lines 198-218).\n9. Figure 8: The biological and clinical interpretation of these findings should be clearly articulated.\nWhat do these data imply for T cell function and therapeutic potential?\nWe have updated our concluding remarks to provide some interpretation around the impact of our\nfindings may have upon the therapeutic potential of EBV-specific T cells (see lines 360-365).\n10. Please specify the biological characteristics of the EBV T cell cultures selected for in vivo validation.\nHow were they chosen?\nThe EBV-specific T cells were selected based upon the availability of material. We also ensured they\nwere selected to represent products that had differential clustering in Figure 9.\n11. The claim regarding CD4+ T cell reconstitution is overstated. This expansion is only evident in the\nspleen, and only TIG.J.2 demonstrates significant CD4+ expansion in other anatomical locations.\nPlease correct this statement.\nWe thank the reviewer for this comment and have corrected this statement in the text.\n12. A safety evaluation of the T cell products is missing. This is a critical component, especially in the\ncontext of allogeneic therapies.\nAs part of our routine cell therapy manufacturing we perform a standard assessment of alloreactivity.\nAll products were below our threshold for alloreactivity (<1% of CD107a+ CD3+ T cells in response to\nHLA-mismatched targets). In our animal monitoring we perform assessment for safety that include\nmeasures for evidence of GVHD. We saw no evidence of GVHD or other safety concerns related to the\nuse of the T cell products. We have now commented on these results in the text (see lines 247-248).\n13. A clearer conclusion should be provided regarding which parameters best predict clinical efficacy\nof EBV-specific T cell products. Ideally, safety assessment should also be considered.\nWe have updated our conclusion to provide more nuance in parameters that may predict clinical\nefficacy. We have not included safety in this conclusion because we saw no evidence of adverse events\nin our animal study, which is consistent with the already well known safety profile of allogeneic EBV-\nspecific T cells in humans (see lines 360-365).\n14. The lack of correlation between T cell proliferation and effector function is an important\nobservation. The authors should discuss potential explanations.\nWe agree that this is an interesting observation. However this requires further investigation before we\nare able to discuss any potential explanations.\n15. Figure 3A: Shouldn’t the overall CDR3 frequency be 100%? Please clarify whether this figure refers\nonly to shared EBV-specific TCRs between PBMC and product and how the frequency is calculated.\nThe products contain some T cells that are not EBV-specific. The only TCR included in the Fig 3A are\nthose defined as EBV-specific following cytokine capture.\n16. Figure 4: The representation of shared CDR3 sequences is unclear. Why are some sequences\napparently shared within the same sample?\nWe thank the reviewer for this observation. We have now corrected this figure to only show sharing\nbetween different products (see Figure 4).\n17. Line 694: The title refers to TRBV diversity, but the figure actually addresses CDR3 diversity and\nsharing. Please correct this. The term TRBV seems to be incorrectly used in place of “CDR3 sequences”\nin several instances (e.g., Line 132). Please revise accordingly. It is important to note that TRBV and\nCDR3 refer to different regions of the T cell receptor and cannot be used interchangeably.\nThis has now been corrected throughout the manuscript.\n18. Figure 6: The cluster at the center of the heatmap appears specifically enriched in the stimulated\ncondition. The authors should discuss the potential biological implications. Additional clusters that\nemerge only in specific conditions should also be described.\nWe have now addressed this issue in the manuscript.\n19. Figure 7A: Some T cell batches display a cytotoxic signature even in the unstimulated condition.\nCould the authors comment on the potential origin or significance of this finding?\nCytotoxic signatures are commonly expressed and maintained in human herpesvirus (EBV/CMV)-\nspecific T cells at rest. This is common in resting T cell populations assessed directly ex vivo in human\nPBMC.\n20) Line 133: The claim of consistency in TCR diversity between replicate batches is not fully accurate.\nFor example, TIG.D replicates do not show clear consistency.\nWe thank the reviewer for their comment. We have now removed this statement from the text.\n21) Were the experiments performed only once? If so, this raises concerns. Technical replicates should\nbe clearly stated.\nWe thank the reviewer for their comment and have added details to the text for experiments that were\nperformed with replicates.\n22) In the functional assays, inclusion of a negative control would strengthen the assessment of T cell\nefficacy. Please indicate if one was used.\nYes all assays use a negative control. These are subtracted from the test values to provide a final result\nvalue.\nResponse to Reviewer #4\nIn their resource paper, Smith and colleagues describe a multi-omics platform to evaluate the\nphenotypic and functional properties of allogeneic EBV-specific T cells, incorporating the analysis of T\ncell repertoires, transcriptomes, and flow cytometry. The overall goal is to establish “potency\nattributes” of EBV-specific T cell therapy batches for clinical studies.\nThe authors have established a repository of allogeneic EBV-specific T cell therapy products. To\nachieve broad HLA coverage, 21 batches were generated from 12 donors recruited through a bone\nmarrow program. The manuscript describes the characterization of each of these batches using a\nvariety of immunological assays, including degranulation, cytokine secretion, proliferation,\ncytotoxicity, T cell repertoire analysis, and gene expression. The relationship between gene\nexpression profiles and other attributes of the EBV-specific T cell therapy batches, such as degranulation,\nproliferation and T cell repertoires was investigated using PCA-based approaches. Finally, 4 of the T\ncell therapy batches were tested in a xenogeneic EBV-LCL tumor model to assess their potency. This\nanalysis revealed a strikingly discordant potency of the T cell therapy products to reduce EBV DNA\nload following adoptive transfer. mice. Unfortunately, the mechanisms underlying this discordant\nactivity are not further investigated or discussed.\nWe appreciate the reviewer’s comment on the mechanisms underlying this discordant activity and\nhave addressed this in the revised version of the results and discussion section (see below).\nMain points:\n1. This study appears to be well designed, and the data could be of interest. The main message\nthis study would like to convey is, however, not clear. The authors have analyzed many immunological\nparameters of the allogeneic EBV-specific T cell therapy batches, but it remains unclear which\nparameter(s) may be associated with potency to be successfully used in adoptive T cell therapy. Given\nthe large difference in therapeutic outcomes observed in recent clinical trials, this information is of\ncritical importance to move this approach through clinical studies.\nWe agree with the reviewer and have now revised the text especially the results and discussion section\nto highlight/identify parameter(s) may be associated with potency to be successfully used in adoptive\nT cell therapy (see lines 222-237 and 343-357).\n2. The integration of the different parameters (Figure 8) should be the key aspect of this study,\nestablishing potentially important correlations with the potency of the T cell therapy products. The\nresults of this part are not clear and key information (scripts, explanation why the PCA approach was\nchosen) are not provided. Alternative approaches of data integration should be tested. This part of\nthe study needs to be improved.\nWe would like to thank the reviewer for her/his advice and have tried to address the limitations\nidentified. We have revised the manuscript text (methods, results and discussion) to integrate different\nparameters (See new Supplemental Table 6 and Figure 8).\n3. The pre-clinical model (Figure 9) is of interest. Do the authors consider that the potency of the\ndifferent EBV-specific T cell therapy batches in the xenogeneic EBV-LCL tumor model may predict\npotency in patients? If so, why did they test only 4 of the 21 batches and why did they not define\nwhich parameters analyzed in Figures 1-7 correlate with potency in the model? There is obviously the\ncaveat that this is a mouse model, however, it would provide a potentially important message and a\ngood basis for further studies.\nYes, we do see that the xenogeneic EBV-LCL tumor model may predict potency in patients. However,\nthe use of broader functional and transcriptional framework outlined in this manuscript will be\nrequired to strengthen this predictability. We did use the parameters analysed in figures 1-7 to select\nfour batches for in vivo studies. We have now revised the results and discussion section to outline this\nin more detail (see lines 239-245).\nSpecific questions:\n4. Figure 2A: data are represented in heat maps to give a condensed overview. Detailed data\ncorresponding to the individual measurements of degranulation and cytokine production should be\nprovided as a supplementary figure or in a supplementary data file.\nAs requested by the reviewer, we have added all corresponding data for Fig. 2A as supplemental data\nfile.\n5. Figures 3 and 4: the analysis has been restricted to TRBV sequences using the ImmunoSEQ platform.\nA clonal analysis including the analysis of TRAV sequences was not performed. What was the reason\nfor this choice?\nAt the time of data generation we decided that the TRBV deep sequencing analysis would provide us\nwill ample information on TCR usage in our products, rather than using a single cell approach to study\nboth TRBV and TRAV.\n6. Figure 6: have the correlations been calculated on the average of all 326 genes or differentially\nexpressed genes between therapy batches? In any case, all gene expression data need to be included\nin a supplementary data file or made publicly available in a database.\nAs requested by the reviewer, we have added all gene expression data as supplemental data file.\n7. Figure 7: according to the methods section, differentially expressed genes were calculated between\nunstimulated and stimulated as well as between cell isolated by cytokine capture and sorting using\nmultimers and top 20 genes were represented. The top 20 genes of which contrast are shown? The\nfigure legend is not helpful.\nWe apologise for this confusion. Differentially expressed genes were determined between samples\nwithin each parameter following house-keeping normalised. We did not compare between stimulated\nand unstimulated. We have corrected the methods section to reflect this. We have updated figure\nlegend of Figure 7 stating that the top 20 differentially expressed genes for each panel is shown.\n8. Figure 8: integration of the different parameters analyzed in this study is a key part of this study,\nbut it is not clear how this analysis was performed. The results section refers to the Methods but no\nparagraph can be found in the methods section. A detailed description of this analysis is essential, and\nscripts should be provided.\nWe have revised the methods and results section to provide more details on data presented in Figure\n8 .\nMinor points:\nFig. 1B: x- and y-axes labels are not readable. Please enlarge or explain in figure legend. NP = no\npeptide?\nWe have updated Figure 1B as recommended by the reviewer.\nThe text would benefit from proofreading.\nWe would like to thank the reviewer and have carefully revised the text.\nReplies to reviewers comments\nWe would like to express our sincere thanks to all four reviewers for their helpful suggestions and\nrecommendations. These comments have been immensely valuable for us to improve our manuscript.\nReviewer #3 (Remarks to the Author):\nAuthors provided appropriate responses / clarifications to my previous comments.\nThe only remaining concern is related to the functional assays. The authors state that in all assays\nnegative controls were used and were subtracted from the test values to provide a final result value.\nHonestly, I would prefer to see them represented separately and not subtracted from the positive\nvalues.\nWe appreciate reviewer’s comment. We respectfully disagree and prefer to keep figures as its. We have\nprovided all raw data including negative controls in the supplemental source data.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed the points I have raised during the first round of reviewing.\nMinor points:\n- Supplementary Table 5 is not included in the supplementary data file\n- Despite my request, x-axis labels have not been added to Fig. 1B\nSupplemental Table 5 was already included in the supplemental files. We have now added this in the\nsupplemental material file.\nWe apologise for the oversight on x-axis labels in Figure 1B. We have now added these labels in the\nfigure."}]

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