Integrative Bulk and Single-Cell Profiling of Premanufacture T-cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-cell Therapy

Chimeric antigen receptor (CAR) T-cell therapy has been a breakthrough in cancer therapy, yet failure to achieve long-term CAR T-cell persistence remains a major barrier to sustained remission in many patients. Although complete response rates in pediatric B-cell acute lymphoblastic leukemia (B-ALL) are high, differences in apheresis T-cell products have been shown to play a critical role in determining the duration of antitumor CAR T-cell function (1). Recent studies have provided strong evidence that differences in apheresed T cells are a major determinant in therapeutic failure of CAR T-cell therapy in adult malignancies such as chronic lymphocytic leukemia (CLL) and multiple myeloma (2, 3), suggesting that a deeper understanding of intrinsic T-cell function is essential for improving existing and future CAR T-cell therapies for both pediatric and adult cancers.

At the core of this challenge is the heterogeneous nature of autologous T cells that form the starting material for CAR T-cell therapy. Most CAR T-cell therapy trials to date have engineered CAR T cells from the bulk population of T cells extracted from the patient, consisting of a mixture of CD4⁺ and CD8⁺ T cells across naïve, memory, and effector lineages. Clinical and preclinical studies have led to a growing understanding of a positive role of naïve and memory T-cell subsets in CAR T-cell efficacy, suggesting that cellular stemness and memory formation are critical to maintaining long-term remission (4–6). These studies have provided foundational insight into the composition of favorable T-cell phenotypes, yet a more comprehensive understanding has been hindered by limited sample sizes, confounding by T-cell subtype, and lack of deeper molecular analyses.

To address these challenges, we developed a subtype-specific transcriptomic atlas of premanufacture T cells from 71 patients on trial to receive anti-CD19 CAR T-cell therapy. A crucial component of our experimental design was to sort T-cell subsets prior to RNA sequencing (RNA-seq), allowing us to directly account for the confounding effect of T-cell subset composition and identify clinically associated molecular pathways that act within T-cell subsets. We developed a web server for visualization of genes and regulons from our transcriptomic atlas, which can be accessed at https://tanlab4generegulation.shinyapps.io/Tcell_Atlas/. On the basis of our findings regarding IFN signaling and TCF7 expression in effector T-cell subtypes, we performed integrative Cellular Indexing of Transcriptomes and Epitopes sequencing (CITE-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on six of the patients in the study, allowing us to more deeply characterize the interplay and regulation of these clinically associated biological pathways.

We identified 71 children or young adults who were enrolled to receive anti-CD19 CAR T-cell therapy at the Children's Hospital of Philadelphia (Supplementary Table S1). The mean age at enrollment was 11.7 [95% confidence interval (CI), 10.1–13.3], and there was a balanced ratio of female and male patients [47.9% (36.0–60.0) female]. Seventy patients had relapsed or refractory B-ALL, and one was a young adult with Hodgkin lymphoma. CAR T-cell manufacture was successful in 65 of the 71 patients. The primary clinical endpoint for functional CAR T-cell persistence was duration of B-cell aplasia (BCA), consistent with prior studies which have established that BCA is a sensitive marker of functional anti-CD19 CAR T-cell persistence in pediatric populations (1, 7). Long-term CAR T-cell persistence was defined as BCA ≥ 6 months, whereas short-term or failed persistence was defined as BCA < 6 months. Duration of BCA was considered to be right-censored if the patient had ongoing BCA at the most recent follow-up, proceeded to bone marrow transplant, died of other causes, or had CD19⁻ relapse with continued BCA. In an intention-to-treat manner, the median duration of CAR T-cell persistence among patients in our cohort was 11 months, similar to event-free survival durations reported in recent clinical trials in B-ALL (8).

To assess the composition and molecular pathways of premanufacture T cells, we curated a biobank of T cells from these 71 patients (Fig. 1A). These T cells were aliquoted at time of clinical leukapheresis and represent the populations of the T cells used for CAR T-cell manufacture. We used FACS to sort T cells into five T-cell subsets: naïve (T_N), stem cell memory (T_SCM), central memory (T_CM), effector memory (T_EM), and effector (T_EFF; Supplementary Fig. S1A). We performed RNA-seq on the five sorted T-cell populations for each of the patients, producing an atlas of 355 transcriptomic profiles representing the T-cell composition across pediatric premanufacture T cells.

We found that higher proportions of T_N, T_SCM, and T_CM were associated with clinical CAR T-cell persistence beyond 6 months [false discovery rate (FDR)–adjusted P = 3.7e-3, 1.3e-2, 1.0e-3], and lower proportions of T_EM and T_EFF were associated with clinical CAR T-cell persistence beyond 6 months (FDR-adjusted P = 4.0e-2, P = 1.1e-3; Fig. 1B). Combined proportions of T_N, T_SCM, T_CM, and T_EM were associated with clinical CAR T-cell persistence, with greatest discrimination observed when combining T_N, T_SCM, and T_CM proportions (log-rank test P = 6.2e-3 to 1.4e-2; Supplementary Fig. S1B and S1C), suggesting a general and robust trend for a role of naïve and memory subsets as a prognostic marker for clinical CAR T-cell function.

We performed CIBERSORTx (9) analysis to estimate the relative proportion in CD4⁺ and CD8⁺ T cells in each T-cell subset. To robustly estimate deconvolution proportions, we ran the algorithm using bulk and single-cell references, finding that estimated proportions were robust and broadly concordant with naïve, memory, and activated T-cell phenotypes based on reference annotations (Supplementary Fig. S2A and S2B). We found that T_N, T_SCM, and T_CM subsets were predominantly CD4⁺, whereas T_EM and T_EFF subsets were predominantly CD8⁺ (Supplementary Fig. S2C). Using the deconvoluted data, we found that a greater proportion of CD8⁺ T_N and T_SCM cells was positively associated with clinical CAR T-cell persistence (FDR-adjusted P = 3.1e-2, 4.1e-2; Supplementary Fig. S2D). This trend was observed both in our deconvolution estimates and in differential expression of CD8 genes (Supplementary Fig. S2E). These data suggest a key role of naïve T cells, particularly in the CD8⁺ compartment, in contributing to CAR T-cell efficacy.

Because the composition of naïve, memory, and effector T-cell lineages was robustly associated with clinical CAR T-cell persistence, we next sought to investigate the biological pathways defining these T-cell subtypes among our patient cohort. Our bulk RNA-seq data on sorted patient T cells captured the functional continuum of T-cell differentiation, from T_N, T_SCM, T_CM, T_EM, and T_EFF subtypes (Fig. 1C). At a global transcriptomic level, between-subtype differences in T cells were the dominant effect; T cells of the same subtype clustered strongly between patients, whereas T cells derived from individual patients generally separated by subtype. T_N and T_SCM shared similar transcriptomic profiles and were largely indistinguishable at the transcriptomic level, whereas T_CM, T_EM, and T_EFF separated strongly.

Differential expression analysis with Linear Models for Microarray Data (Limma; refs. 10, 11) revealed 6,953 differentially expressed genes associated with T-cell type (FDR < 0.05; Supplementary Table S2). Pathway analysis of the differentially expressed genes revealed an enrichment in lymphocyte activation, cytokine signaling, and leukocyte migration pathways (P = 1.93e-34, 1.49e-23, 2.54e-21; Fig. 1D); single-sample Gene Set Enrichment Analysis (ssGSEA; ref. 12) revealed that these pathways were enriched among the effector T-cell lineages (Supplementary Fig. S3A).

We found that expression of lymphocyte proliferation pathways increased along a gradient toward effector T cells (P = 2.5e-15, Fig. 1D and E), associated with higher Ki-67 expression in T_EM and T_EFF cells (Supplementary Fig. S3A). Effector T cells exhibited extensive metabolic reprogramming supportive of a functionally activated state. Expression of LDHA, GLUT1, and GAPDH was upregulated among effector T cells, indicating a metabolic shift to aerobic glycolysis, and pathways involving biosynthesis of protein, DNA, and lipid were enriched, suggesting biosynthetic support for cellular division (Supplementary Fig. S3B). Counteracting these pathways, we found that apoptotic pathways, including both intrinsic and extrinsic apoptotic signaling, were strongly enriched among genes expressed higher in the effector lineages (P = 2.6e-12, Fig. 1F; Supplementary Fig. S3C and S3D). Upregulated expression of cytotoxic markers, cytokines, and inhibitory receptors among T_EM and T_EFF cells suggests that these subsets likely represent functional, endogenously activated T-cell phenotypes (Supplementary Fig. S4A and S4B). Together, these data suggest that although the more differentiated T cells exhibited a proliferative phenotype at baseline, their proliferative ability may be inherently limited by apoptotic pathways that act as a barrier to the years-long cellular survival and proliferation necessary for long-term success of CAR T-cell therapy.

Next, we sought to investigate the transcriptional regulators that act to maintain the naïve and early memory phenotypes associated with long-term CAR T-cell persistence. We sought to construct a robust transcriptional regulatory network (TRN) consisting of predicted interactions between transcription factors (TF) and target genes. Benchmarking studies for TRN inference have demonstrated that no individual computational method performs optimally across data sets, and a consensus approach achieves superior and more robust performance (13). Following this principle, we applied top-performing gene-regulatory inference algorithms (14–17) to our expression data to generate base networks, from which we constructed a consensus TRN using the Borda Count principle as described reviously (13). To identify T cell–specific TF–gene interactions, we repeated these steps to construct a consensus TRN for non–T-cell immune populations from a compendium of 43 microarray datasets from Becht and colleagues (18), representing 708 samples, and defined a T cell–specific TRN as the network whose edges are present in the T-cell TRN but not present in the non–T-cell immune TRN from public data (see Methods). To validate this network approach, we considered a set of T-cell TFs involved in T-cell differentiation (19) as a benchmark, and assessed the node degree of these TFs in the T cell–specific network and the network constructed from non–T-cell public immune data. The node degree of these benchmark TFs was significantly higher in the T cell–specific network compared with the null distribution (Wilcoxon rank-sum P = 7.6e-5; Supplementary Fig. S5A), but not different from the null distribution in the non–T-cell immune network (P = 0.32), demonstrating the capacity of the constructed T cell–specific network to capture T-cell transcriptional regulatory biology.

We have previously described a method for identifying key TFs involved in regulating transcriptional fate, which we have recently applied to study hematopoiesis and type I diabetes (20–22). We extended this method to our T cell–specific network to identify key TFs acting to maintain naïve and stem cell memory state, as well as TFs driving effector T-cell development. Among the TFs with the strongest predicted regulatory potential driving naïve and early memory T-cell state were TCF7 and LEF1 (FDR < 0.05; Fig. 2A and B), which have been previously described as essential in early thymocyte development (23, 24) and maintenance of T-cell memory function (19, 25). Among the TFs with the strongest regulatory potential for effector T-cell states were TBX21 (T-bet), which has been associated with both effector CD8⁺ and CD4⁺ Th1 differentiation (19, 26, 27); PRDM1 (Blimp-1), which has been associated with differentiation of CD8⁺ cells and non-Tfh CD4⁺ cells (19, 26, 28); and ZEB2, a transcriptional repressor that has been recently shown to cooperate with T-bet to promote a terminal CD8⁺ T-cell differentiation program (refs. 29, 30; FDR < 0.05; Fig. 2C and D). These were supported with strong differential expression of these TFs across T-cell subtype (Fig. 2E; Supplementary Fig. S5B–S5F).

We designed a mixed-effects regression model to identify differentially expressed genes within T-cell subtypes associated with clinical CAR T-cell persistence (Supplementary Table S2; Methods). This approach allowed us to identify genes and pathways associated with clinical CAR T-cell persistence in a manner that accounts for the potential confounding effect of T-cell subtype composition (Fig. 3A). We performed pathway analysis on the differentially expressed genes and found that type I IFN signaling response was the most significantly enriched pathway (Fig. 3B, P = 2.67e-16). The genes most differentially upregulated among patients with poor CAR T-cell persistence included the type I IFN response genes RSAD2, IRF7, MX1, ISG15, OASL, and IFIT3 (FDR < 0.05; Fig. 3A). Interestingly, these genes were differentially expressed between patients even among the naïve and memory T-cell subtypes (Fig. 3C).

We extended our gene network inference methods to define S_regressed, a T cell–specific network generated from expression data regressed for the potentially confounding effect of T-cell subtype (see Methods). We applied our TF prioritization method to identify TFs associated with clinical CAR T-cell persistence. Our network highlighted IRF7 as the gene with the strongest predicted regulatory potential discriminating between clinical CAR T-cell persistence (Fig. 3D and E). Among other top hits were STAT4, which has also been associated with IFN response signaling and Th1 differentiation (27, 31), and SOX4, which has been associated with T-cell differentiation (32).

Type I IFN response genes have been shown to play an important role in physiologic T-cell activation (33); indeed, we found that the type I IFN response pathway was upregulated in the T_EM and T_EFF (Supplementary Fig. S6A). We asked whether this pathway was upregulated among patients with poor CAR T-cell persistence in the naïve and early memory subsets. To assess the robustness of this pathway, we repeated our differential expression analysis on T-cell subsets that both included and excluded the T_EM and T_EFF populations. IRF7, RSAD2, MX1, and ISG15 were broadly differentially expressed among all T-cell subsets, and IFN genes were among the top genes associated with poor CAR T-cell persistence in comparisons including strictly naïve and early memory T-cell subsets (Supplementary Fig. S6B). For example, in our mixed-effects interaction model, we identified 16 genes associated with clinical CAR T-cell persistence within the T_N, T_SCM, and T_CM subtypes, seven of which were negatively associated with clinical CAR T-cell persistence: IRF7, RSAD2, SLC7A5, OASL, TYMP, MX1, and ISG15 (Supplementary Fig. S7A). We found that among these early memory T-cell subsets, IRF7 was the top-ranked TF associated with poor clinical CAR T-cell persistence, suggesting that the IFN signaling response pathways were enriched across T-cell phenotypes, including the naïve and early memory subsets (Supplementary Fig. S7B–S7D).

We next hypothesized that differential molecular pathways between patients with long and short CAR T-cell persistence may manifest in transcriptional differences in the effector T-cell phenotypes. We focused on the T_EM and T_EFF subsets, identifying genes (Fig. 4A) and TFs (Fig. 4B) differentially expressed between patients with short and long CAR T-cell persistence. We observed an enrichment of IFN signaling response pathways (P = 2.49e-8), and the most differentially expressed genes associated with poor CAR T-cell persistence were IFN response genes including RSAD2, MX1, and IFIT3 (Fig. 4A). Unlike our previous analyses where IFN response pathways dominated the differential expression analysis, the highest-ranked pathways for T_EM and T_EFF were those associated with T-cell activation and differentiation (P = 3.34e-13; Fig. 4C), suggesting that regulators of T-cell differentiation state may have an additional role in maintaining T-cell persistence within these differentiated subtypes.

Narrowing our differential expression analysis to TFs in these effector phenotypes, we found that TCF7 was the most significantly upregulated TF associated with long CAR T-cell persistence (FDR = 0.018, Fig. 4B). Network analysis revealed that TCF7 was among the top-ranked TFs associated with CAR T-cell persistence in T_EM and T_EFF; among the other top hits expressed at higher levels among patients with long CAR T-cell persistence were BACH2, FOS, and GATA3, which is associated with Th2 development; among top hits expressed at higher levels in patients with short CAR T-cell persistence was STAT1, which has been associated with Th1 development and IFN signaling (refs. 27, 34; Fig. 4D and E). Strictly within the T_EFF subset, we found TCF7 was the most significantly upregulated TF associated with long CAR T-cell persistence, and T-cell activation remained the top enriched pathway (Supplementary Fig. S8A–S8D). We repeated our network analysis using exclusively T_EFF expression data, and found that TCF7 remained among the top 20 ranked TFs, along with GATA3 and BACH2 (Supplementary Fig. S8E and S8F).

We next sought to validate transcriptional gene signatures representing the TCF7 regulon and IFN signaling response (Fig. 5A). During network construction from our complete transcriptome dataset involving all T-cell subtypes, TCF7 was the transcription factor with the greatest node connectivity, with 2,496 predicted targets, of which 1,487 had a positive expression correlation with TCF7. We defined the TCF7 regulon as the set of predicted target genes of TCF7 with a positive expression correlation, and defined the TCF7 regulon score as the single-sample GSEA enrichment score using the gene set composed of TCF7 and its regulon (Supplementary Table S3). Because this score was defined in a manner agnostic to T-cell type or clinical outcome labels, we first assessed whether this score discriminated between naïve and T_EFF cell types, as well as between clinical response groups among T_EFF cells. Indeed, this TCF7 regulon score, discriminated between T-cell subtypes, was significantly higher in T_EFF among patients with long-term CAR T-cell persistence, and discriminated between these clinical outcome groups using leave-one-patient-out and leave-one-T-cell-type-out cross-validation (Supplementary Fig. S9A–S9D). Next, we sought to assess this score in an independent validation set. We assessed this score on the RNA-seq dataset of Fraietta and colleagues, which consisted of preinfusion anti-CD19 CAR T cells generated from adults with CLL (5) and is, to our knowledge, the largest clinically annotated RNA-seq dataset from patients receiving CAR T-cell therapy prior to this study. The CAR T cells of these 34 patients assessed either underwent mock stimulation (non–CAR-stimulated group) or bead-based anti-CD19 CAR stimulation in vitro (CAR-stimulated group). This data set had several differences from ours, because it was generated from engineered CAR T cells, collected from adults with CLL, and was not sorted by T-cell subtype. Despite these differences, we sought to evaluate whether our gene signatures were robust enough to validate in this independent data set. Indeed, we found that our TCF7 regulon score was positively associated with favorable clinical response in the non–CAR-stimulated samples (P = 0.0062; Fig. 5B–D), as well as in the CAR-stimulated group (P = 0.017). The discriminative ability of this gene signature even in the CAR-stimulated group suggests that the prognostic relevance of TCF7 regulon is maintained throughout CAR T-cell manufacture and stimulation.

Next, we developed a prognostic gene signature representing the IFN response observed across T-cell subtypes. We identified 55 genes significantly upregulated among those patients with short CAR T-cell persistence, which was strongly enriched for type I IFN response pathways (Fig. 3A and C). We thus defined an IFN response score as the single-sample GSEA enrichment score based on these differentially expressed genes (Supplementary Table S3). This gene signature was associated with T-cell subtypes and was upregulated among the T_CM, T_EM, and T_EFF subsets compared with T_N (Supplementary Fig. S9E). We performed cross-validation based on a leave-one-patient-out and leave-one-T-cell-type-out approach, finding that this method for gene signature discovery robustly discriminated between patients with long-term and short-term CAR T-cell persistence across T-cell subtypes [area under ROC Curve (AUROC) = 0.63–0.76; Supplementary Fig. S9F and S9G]. We next assessed this gene signature on the independent validation set by Fraietta and colleagues (5). We found that for the non–CAR-stimulated group, our IFN response signature was significantly associated with poor clinical response (P = 0.035, AUROC = 0.71; Fig. 5E). However, for the CAR-stimulated group, this gene signature was not significantly prognostic (P = 0.92, AUROC = 0.54; Fig. 5F and G), suggesting that the T-cell activation pathways triggered by CAR stimulation may negate the prognostic significance of the IFN response signature.

We asked to what extent the engineered CAR T cells differ from premanufacture T cells and retain an association with long-term clinical CAR T-cell persistence. We identified engineered CAR T cells from 11 patients on trial to receive CAR T-cell therapy (study identifier: NCT01626495) who were not previously studied in our bulk RNA-seq analysis: four had long-term BCA greater than or equal to 6 months, five had short-term BCA less than 6 months, and two were not ultimately infused due to medical contraindications. We identified premanufacture T cells from three of these patients. FACS analysis revealed increased protein expression of IRF7 in the CAR T-cell products compared with premanufacture T cells across T-cell subsets (FDR = 0.001–0.062; Supplementary Fig. S9H), suggestive of IFN signaling induced during the CAR T-cell manufacturing process. Among the postmanufacture CAR T-cell samples from the 9 patients who received a CAR T-cell infusion, proportions of T-cell subsets defined by CD62L and CD45RO were not significantly associated with long-term CAR T-cell persistence (FDR = 0.948; Supplementary Fig. S9I). RNA expression assessed by qRT-PCR of TFs was assessed for association with clinical CAR T-cell persistence, with expression of TCF7 trending toward higher expression in patients with long CAR T-cell persistence, IRF7 and TBX21 trending toward higher expression in patients with short CAR T-cell persistence, and no clear trend for LEF1 and PRDM1 expression; these trends were not statistically significant (P = 0.161-0.794; Supplementary Fig. S9J and S9K). The relatively small sample size likely contributed to the lack of statistical significance in these trends; power analysis suggested that 24, 22, and 17 patients per group would be required to achieve statistical significance at 0.05 with 80% power for expression of TCF7, IRF7, and TBX21, respectively. In addition, in vitro stimulation during CAR T-cell manufacture may at least partially abrogate between-patient T-cell differences, as reported in a previous study (1).

To more deeply understand the relationship between the TCF7 regulon and IFN response in premanufacture T cells, we performed CITE-seq (35) and scATAC-seq on 6 of the 71 patients included in this study. These 6 patients were selected on the basis of sample availability and representing a range of clinical CAR T-cell persistence outcomes, from failure at 2 months to persistence greater than 22 months. From our CITE-seq data, we obtained a dual readout of cell surface protein expression and RNA quantification within the same cell; scATAC-seq was performed in a separate aliquot of T cells from the same patients. After filtering out low-quality cells, our single-cell data consisted of 17,750 and 19,673 cells from the CITE-seq and scATAC-seq data, respectively. We first focused on the CITE-seq data, performing data integration, dimensionality reduction, and clustering to reveal a single-cell landscape of CD4⁺ and CD8⁺ T cells (see Methods; Fig. 6A–C; Supplementary Fig. S10A). The data broadly mixed by patient and separated by both RNA and protein markers, suggestive of good integration (Supplementary Fig. S10B and S10C). The single-cell RNA-seq (scRNA-seq) data were initially clustered into 21 clusters based on global transcriptomic profiles, and clusters were subsequently merged on the basis of selected RNA and protein markers (Fig. 6C). This led to the identification of 11 final T-cell subsets, including CD4⁺ and CD8⁺ naïve, memory, and effector subsets, as well as resting and activated regulatory T cells (Treg cells) highly expressing the transcription factor FOXP3. The proportion of naïve and early memory T cells ranged from 92.6% in patient 51 with greater than 6 months of CAR T-cell persistence, to 72.7% in patient 66 with failed CAR T-cell persistence at 3 months (Supplementary Fig. S10D and S10E).

Intriguingly, we identified a cluster of CD4⁺ T cells expressing naïve and memory markers such as CCR7, CD62L, and CD45RA, but also strongly expressing IFN response genes such as IRF7, RSAD2, MX1, and ISG15 as well as STAT1, which has been shown to be involved in IFN signaling and Th1 development (refs. 27, 34; Fig. 6A and D; Supplementary Fig. S10F and S10G). This cluster did not express other Th1 TFs, such as STAT4 or TBX21, and did not express characteristic Th2 or Treg TFs, such as GATA3, STAT6, or FOXP3 (Supplementary Fig. S10G). This cluster was relatively rare (approximately 1% of total T cells), and not significantly associated with CAR T-cell persistence in the n = 6 patients assessed (P = 0.40). However, we reasoned that this population may be helpful in understanding the relationship between the TCF7 regulon and IFN response pathway. We performed AUCell analysis (36) to define single-cell enrichment scores for the previously defined TCF7 regulon and IFN response gene signatures. We found that the TCF7 regulon score was enriched among naïve and memory T cells, whereas the IFN response gene signature was strongly enriched among activated T cells, particularly CD8⁺ T_EM and T_EFF (Fig. 6E). These gene signatures shared a strong inverse relationship among the T-cell subtypes, with the notable exception of the IFN-responsive CD4⁺ naïve/memory population, which was strongly enriched for both signatures (Fig. 6F). These data provide cellular level support for our prior finding that IFN response plays a role not only in activated T-cell states, but in naïve and memory states. Furthermore, this finding suggests that the TCF7 regulon and IFN response pathway are not mutually exclusive, and may impart independent effects of T-cell function and CAR T-cell efficacy.

We investigated the epigenetic regulation of T-cell states and TCF7 function through integration of scATAC-seq data with CITE-seq data. We integrated patient scATAC-seq data using Seurat and projected these data onto the UMAP defined by the CITE-seq analysis. We assessed chromatin accessibility of TF motifs with chromVAR (37), finding that accessibility of the TCF7/LEF1 motif was strongly associated with naïve and memory T-cell states, whereas accessibility of the PRDM1 (Blimp-1) motif was associated with effector memory and effector T cells, and accessibility of the TBX21 motif was strongly associated with CD8⁺ T_EM and T_EFF (Fig. 7A and B). The strongest differentially accessible motif was the AP-1 motif, which was closed among naïve T cells and strongly opened among the T_EM and T_EFF subsets (Fig. 7A; Supplementary Fig. S11A).

While TCF7 expression and motif accessibility were diminished among T_EFF cells (Fig. 7B), we asked whether its enhancer accessibility and downstream targets remained partially active, which could provide an explanation for the positive association of TCF7 with clinical CAR T-cell persistence observed in our bulk RNA-seq analysis. We predicted enhancer–promoter interactions using two methods: chromatin coaccessibility from scATAC-seq alone using Cicero, and correlation of chromatin accessibility with RNA expression using a metacell-based regression approach as described previously (38). Using stringent cutoffs of Cicero coaccessibility scores (>0.25) and metacell-based Bonferroni-adjusted P values (<0.05), we identified three upstream enhancers with strong evidence of interaction with the TCF7 promoter (Fig. 7C). We repeated this analysis with the single-cell data from CD8⁺ T_EFF alone, for which gene expression as well as chromatin accessibility of TCF7 and its enhancers was diminished. Despite the use of this restricted subset of cells, we found that one of the enhancers retained both chromatin coaccessibility and association with TCF7 expression (coaccessibility score = 0.37, Bonferroni-adjusted P = 4.3e-3), indicating that the epigenetic regulation of TCF7 remains partially active in this differentiated T-cell subset (Fig. 7C). Physical interactions between predicted enhancer–promoter interactions were validated using chromosome conformation capture in healthy donor T cells, demonstrating that these predicted enhancers were strongest among the naïve and early memory T-cell types and partially maintained across T-cell subtypes (Supplementary Fig. S11B and S11C; Supplementary Table S4).

Finally, we asked whether our data support the notion that TCF7 partially maintains its regulatory interaction with its downstream targets in T_EFF cells. We considered the set of chromatin peaks containing the TCF7 motif, and the genes within the TCF7 regulon defined by our bulk RNA-seq analysis. We first considered all T cells in our scATAC-seq data, finding that the chromatin coaccessibility scores associated with these TCF7 peak–promoter pairs were enriched compared with the null distribution of non–TCF7-related promoter–peak pairs (Supplementary Fig. S11D and S11E; Wilcoxon P = 9.01e-22). We repeated this analysis restricted to the scATAC-seq data associated with the CD8⁺ T_EFF data, asking whether the enrichment of TCF7 regulatory interactions is maintained. While the overall mean coaccessibility score was diminished within this restricted T-cell subset compared with the pan–T-cell analysis, we found that the coaccessibility scores for TCF7 peak-to-promoter pairs remained enriched compared with the null peak-to-promoter scores within the CD8⁺ T_EFF subset alone (Supplementary Fig. S11E; P = 2.73e-3). Furthermore, the mean expression of TCF7 targets based on peak-to-promoter pairs within CD8⁺ T_EFF was significantly greater than the background of non-TCF7 target genes (P = 3.07e-22; Supplementary Fig. S11F). These data support the notion that the regulatory interactions between TCF7 and its targets are partially maintained even among the CD8⁺ effector T cells, providing a mechanistic explanation for our observation of TCF7 expression as a positive association with clinical CAR T-cell persistence in T_EFF cells.

To date, studies of the T-cell determinants of CAR T-cell persistence have been limited by modest clinical sample sizes and confounded by T-cell subtype composition. By performing subtype-specific transcriptomic analysis of 71 pediatric patients and integrative single-cell analysis of six patients, we developed, to our knowledge, the largest and most comprehensive molecular portrait of the landscape of autologously derived T cells in CAR T-cell therapy. Our approach allowed us to deeply characterize the composition and transcriptional regulation of favorable T-cell states, and, importantly, to account for the confounding effect of T-cell subtype to identify TCF7 and IFN signaling as pathways associated with CAR T-cell persistence.

We found that higher proportions of naïve and early memory T cells were positively associated with long-term clinical CAR T-cell persistence. While T_EM and T_EFF subsets were highly enriched in proliferative and metabolically active pathways—phenotypes that may superficially appear to be beneficial for effective CAR T-cell function—we observed concomitant enrichment in intrinsic and extrinsic apoptotic signaling in these subsets, suggesting that programmed cell death ultimately hinders the contribution of these T cells to long-term CAR T-cell persistence. Regulatory network analysis nominated TCF7 and LEF1 (in contrast to other TCF family members) as core TFs maintaining naïve and memory T-cell states, and PRDM1 and TBX21 are associated with T_EFF cell states; the epigenetic basis of these lineage factors was strongly supported by motif enrichment analysis in our scATAC-seq analysis. Given the evidence of a favorable role of naïve and early memory T cells in other CAR constructs (39) and in intrinsic T-cell expansion potential (4), these transcriptional regulators are likely of general importance to other forms of CAR T-cell therapy.

The TCF7 gene, encoding for the Tcf1 TF, has been shown to profoundly remodel the T-cell chromatin landscape, and knockout studies have provided evidence that it acts as a key initiating factor in early thymocyte development (23, 40). Our scRNA-seq and chromatin accessibility suggest a profound shift in the transcriptional regulatory machinery, from TCF7 and LEF1 to PRDM1 and TBX21, with TBX21 expression and motif accessibility particularly notable among CD8⁺ cells. Whether one or two TFs represent pioneering TFs that drive the rest, or if these cell fate transitions represent a cooperative TF circuit, remains unclear (19). Intriguingly, our data revealed several lines of evidence suggesting that TCF7 plays a role not only in maintaining the naïve and early memory T-cell states, but also in maintaining a favorable phenotype in effector lineages. While TCF7 has been known to be highly expressed in naïve and memory T-cell subsets, recent mouse models of chronic viral infection and solid tumors have implicated TCF7 in cell fate decisions in effector and exhausted phenotypes. For example, recent experimental studies have led to the discovery of PD1⁺Tcf1⁺Tim3⁻ progenitor exhausted T cells; TCF7 appears to be a critical regulator of maintaining stemness in exhausted T cells, and may be responsible for the proliferative burst seen in anti–PD-1 therapy (41–43). Our findings represent a key translation to a novel clinical domain, suggesting that TCF7 acts within T_EFF cells to maintain a favorable T-cell phenotype not only in chronic viral infection and solid tumors, but in other forms of immunotherapy including adoptive T-cell immunotherapy.

The IFN response pathway has been shown to play a major role in T-cell function and dysfunction, and its role depends strongly on the context and duration of response. Although IFN signaling stimulates T-cell responses in the short term, chronic IFN signaling has been shown in experimental models to drive T-cell dysfunction (44). IRF7, a key regulator of the type I IFN response (45), emerged in our data as a transcriptional regulator associated with poor CAR T-cell persistence across T-cell subsets. Our data suggest an immunosuppressive effect of chronic IFN signaling among premanufacture T cells that contributes to failure of long-term CAR T-cell persistence. IFN signaling was associated with poor CAR T-cell persistence even in naïve and early memory T cells, suggesting that this pathway is likely driven not by repeated cognate antigen binding, as in T-cell exhaustion, but by alternative mechanisms such as circulating inflammatory factors. Mouse models of persistent LCMV infection have shown that blockage of type I IFN is associated with improved T-cell responses (33, 46), highlighting the immunosuppressive effect of chronic IFN signaling. In contrast, Zhao and colleagues found that CAR stimulation strongly induces IRF7 expression, and that knockdown of IRF7 reduced the cytolytic potential of CAR T cells (47). This suggests that during the acute phase of CAR-stimulated T-cell activation, IRF7 and the type I IFN network may be necessary for optimal activation and stimulation.

Understanding the T-cell determinants of successful CAR T-cell therapy is fundamental in improving clinical outcomes for pediatric B-cell malignancies, as well as in the development of adoptive T-cell therapies for other malignancies including solid tumors. By analyzing autologously derived, premanufacture T cells, our unique data set provides valuable insights for generalizable T-cell mechanisms that are not confounded by specific perturbations in the CAR T-cell manufacturing process. Moreover, our analysis of sorted T-cell subsets and single-cell analyses enabled us to identify gene regulatory mechanisms that account for the potential confounding effect of T-cell subtype composition. Together, our data provide an expanded view of the molecular mechanisms associated with the long-term efficacy of clinical CAR T-cell therapy.

Patients were identified by the clinical practices at the Division of Oncology, Children's Hospital of Philadelphia (Philadelphia, PA). Patients were enrolled onto Children's Hospital of Philadelphia Institutional Review Board (IRB)–approved clinical trials NCT01626495 and NCT02906371, with written informed consent obtained by patients or their guardians in accordance with the U.S. Common Rule. Premanufacture T cells were acquired from the IRB-approved local institutional trial CHP-784.

For event-free survival analyses, we defined a CAR T-cell failure event as the clinical progression of leukemic blast cells or detection of circulating B cells. Event-free survival was recorded as a censored data point if the patient had BCA and proceeded to receive a bone marrow transplant, died of other causes such as cytokine release syndrome, had CD19⁻ relapse, or had continued BCA at the most recent follow-up.

T cells were enriched from apheresis samples by negative selection using EasySep Human T Cell Enrichment Kit (#19051; StemCell Technologies) according to the manufacturer's instructions. Previous work (48, 49) has demonstrated that CCR7, CD62L, CD45RO, and CD95 can be used to differentiate the various T-cell phenotypes by using the following expression patterns: T_N – CCR7⁺, CD62L⁺, CD45RO⁻, CD95⁻; T_SCM – CCR7⁺, CD62L⁺, CD45RO⁻, CD95⁺; T_CM – CCR7⁺, CD62L⁺, CD45RO⁺, CD95⁺; T_EM – CCR7⁻, CD62L⁻, CD45RO⁺, CD95⁺; T_EFF – CCR7⁻, CD62L⁻, CD45RO⁻, CD95⁺. Briefly, cells were resuspended in FACS buffer (Ca⁺⁺ and Mg⁺⁺ free PBS + 1% BSA), then incubated with CCR7 antibody for 20 minutes at 37°C, washed once, and then incubated with remaining antibody cocktails for 25 minutes at 4°C. Samples were then washed twice, and sorting was done using MoFlo Astrios EQ High-Speed Cell Sorter (Beckman Coulter, Inc). The antibodies used for cell sorting described above were CCR7-FITC (#561271; BD Biosciences), CD95-PE (#556641; BD Biosciences), CD45RO-BV421 (#304224; BioLegend), and CD62L-PerCP/Cyanine5.5 (#304824; BioLegend).

Cells were sorted into TRIzol-LS (Invitrogen), and total RNA was purified using RNeasy Micro Kit (cat no. 74004, Qiagen) and analyzed for purity and integrity using RNA 6000 Pico Kit for Bioanalyzer 2100 (catalog no. 5067–1513, Agilent). Full-length cDNA was synthesized and amplified using SMART-seq V4 Ultra Low Input RNA Kit (catalog no. 634891, Takara) from 1 ng total RNA per sample. Full-length cDNA was purified using SPRIselect beads (catalog no. B23318, Beckman Coulter). Sequencing libraries were prepared using Nextera XT DNA Library Prep Kit (catalog no. FC-131–1096) and 200 pg purified full-length cDNA per sample. Libraries were sequenced on an Illumina Hiseq 2500 in paired-end mode with the read length of 100 nt.

Sorted cells were blocked with Human TruStain FcX (BioLegend, catalog no. 422301) and then stained with a TotalSeq-A antibody panel (see Supplementary Data). Stained cells were immediately processed using the 10x Genomics Chromium controller and the Chromium Single Cell 3′ reagent kits (V3). 3′ GEX libraries were constructed using 10x Genomics library preparation kit. ADT libraries were constructed using KAPA HiFi HotStart ReadyMix kit (Kapa Biosystems, catalog no. KK2601). Library quality was checked using Agilent High Sensitivity DNA kit and Bioanalyzer 2100. Libraries were quantified using dsDNA High-Sensitivity (HS) assay kit (Invitrogen) on Qubit fluorometer and the qPCR-based KAPA quantification kit. Libraries were sequenced on an Illumina Nova-Seq 6000 with 28:8:0:87 paired-end format.

Sorted cells were centrifuged at 300 × g for 5 minutes at 4°C. Forty-five microliters of chilled lysis buffer was added to cell pellets and mixed by pipetting gently three times, and incubated 3 minutes on ice. After incubation, 50 μL of prechilled wash buffer was added without mixing and centrifuged immediately at 300 × g for 5 minutes at 4°C. Ninety-five microliters of supernatant was carefully discarded and 45 μL prechilled diluted nuclei buffer (10x Genomics) was added without mixing and sample was centrifuged at 300 × g for 5 minutes at 4°C. The nuclei pellet was then resuspended in 7 μL prechilled diluted nuclei buffer, and nuclei concentration was determined using a Countess II cell counter (Invitrogen). Nuclei (7,000–20,000) were used for the transposition reaction in bulk, and then loaded to the 10x Genomics Chromium controller and processed with the Chromium Single Cell ATAC Reagent Kit. Library quality was checked using Agilent High Sensitivity DNA Kit and Bioanalyzer 2100. Libraries were quantified using dsDNA High-Sensitivity (HS) assay kit (Invitrogen) on Qubit fluorometer and the qPCR-based KAPA Quantification Kit. Libraries were sequenced on an Illumina Nova-Seq 6000 with 49:8:16:49 paired-end format.

Bulk and single-cell data have been submitted to dbGaP under Study Accession phs002323.v1.p1.

Additional methods are described in the Supplementary Methods.

S.A. Grupp reports grants, personal fees, and other support from Novartis; grants from Kite and Servier, grants and other support from Vertex; personal fees from Roche, GSK, Humanigen, CBMG, and Janssen/JnJ; and other support from Jazz, Adaptimmune, TCR2, Cellectis, Juno, Allogene, and Cabaletta outside the submitted work; in addition, S.A. Grupp has a patent for Toxicity management for antitumor activity of CARs, WO 2014011984 A1 issued. D.M. Barrett reports he began work while employed at the Children's Hospital of Philadelphia. Between the time of submission and acceptance, D.M. Barrett left CHOP and is now an employee of Tmunity Therapeutics, Inc. D.M. Barrett reports that his employment had no relation to the submitted work and no financial conflicts are present. No disclosures were reported by the other authors.

G.M. Chen: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. C. Chen: Resources, data curation, investigation, writing–review and editing. R.K. Das: Data curation, investigation, writing–review and editing. P. Gao: Data curation, investigation, writing–review and editing. C. Chen: Data curation, investigation, writing–review and editing. S. Bandyopadhyay: Formal analysis, validation. Y. Ding: Writing–review and editing. Y. Uzun: Software, methodology. W. Yu: Software, methodology. Q. Zhu: Software, methodology. R.M. Myers: Investigation. S.A. Grupp: Conceptualization, resources, writing–review and editing. D.M. Barrett: Conceptualization, resources, supervision, funding acquisition, investigation, writing–original draft, project administration, writing–review and editing. K. Tan: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, writing–original draft, project administration, writing–review and editing.

We thank the Children's Hospital of Philadelphia Flow Cytometry Core for their assistance with cell sorting, and the Research Information Services for providing computing support. This work was supported by NIH grants CA232361 (to D.M. Barrett and S.A. Grupp) and CA233285 (to K. Tan), a Doris Duke Charitable Foundation Clinical Scientist Development Award and a Stand Up To Cancer Innovative Research Grant, Grant Number SU2C-AACR-IRG 12-17 (to D.M. Barrett), a CIHR Doctoral Foreign Study Award #433117 (to G.M. Chen), a NIH/National Child Health and Human Development award T32HD043021, and NCI award K12CA076931 (to Y. Ding), NIH Medical Scientist Training Program T32 GM07170 (to S. Bandyopadhyay), and a 2020 Blavatnik Family Fellowship in Biomedical Research (to Q. Zhu). Stand Up To Cancer (SU2C) is a division of the Entertainment Industry Foundation. The indicated SU2C research grant is administered by the American Association for Cancer Research, a scientific partner of SU2C.