Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma

Chimeric antigen receptor T cells (CAR-T cells) are T cells engineered with a chimeric antigen receptor to specifically lyse tumor cells expressing the targeted antigen. The safety and efficacy of CD19 CAR-T cell products in B-cell malignancies have led to FDA authorization of four products for the treatment of pediatric B-cell acute lymphoblastic leukemia (B-ALL) and B-cell subtypes of non-Hodgkin lymphoma (NHL). The CAR construct designs used in these applications contain intracellular CD3ζ with either 4-1BB (4-1BB.CAR) or CD28 (CD28.CAR). The 4-1BB intracellular domain is thought to convey resistance to exhaustion and superior CAR-T cell persistence in comparison with the CD28 intracellular domain, which exhibits greater short-term activity and adverse events (1–3). Here, a 4-1BB design was applied in the generation of CAR-T cells for the treatment of relapsed/refractory NHL.

Although approximately 50% of patients with NHL undergo remission after initial chemotherapy, prognosis for chemorefractory disease is poor without intervention with adoptive cell therapies (4–7). Following CD19 CAR-T cell therapy, approximately 30% to 40% of patients with NHL will have durable remissions (8, 9). Mechanisms of early relapse or refractory disease remain inconclusive, with significant variation between studies due to differences such as disease type, product design, and product manufacture (10–14). Proposed mechanisms of resistance include poor T-cell quality, T-cell exhaustion, antigen loss/modulation, host factors, and the tumor microenvironment (15). Although several studies have focused on comparisons of infusion products, detailed single-cell studies evaluating postinfusion CAR-T cell transcriptional profiles and phenotypes associated with clinical outcomes remain lacking (16, 17). We address this gap by evaluating CAR-T cells from serial pre- and postinfusion samples in patients with both favorable and poor outcomes by applying methods to assess single-cell transcriptomics and cell-surface protein expression in individual cells.

To describe the evolution of CAR-T cells after infusion into patients with NHL and to identify mechanisms and biomarkers of response, our initial study examined manufactured CAR-T cell products and isolated CAR-T cells from postinfusion blood samples from patients treated for CD19⁺ relapsed/refractory B-cell lymphoma with known outcomes (Supplementary Table S1). For this analysis, we used the single-cell methods single-cell RNA sequencing (scRNA-seq) and/or flow cytometry to investigate time points after infusion that are known from previous studies to be associated with peak expansion (day 14) and contraction (day 30) and represent key changes in CAR-T cell activity (ref. 18; study schema depicted in Fig. 1A; a checklist of which assays were performed with each sample is provided in Supplementary Table S1). Overall, the study used 14 manufactured CAR-T cell products, 14 samples from day 14, and 13 samples from day 30. This sampling represents 13 patients with a favorable response [complete or partial remission (CR or PR)] and 4 patients with a poor response [stable or progressive disease (SD or PD); details of patient clinical information are shown in Supplementary Table S2]. In addition, our data are supported by a commercial product validation data set that consists of 4 samples from day 14 and 3 samples from day 30 from 5 individual patients.

To isolate CAR-T cells for scRNA-seq, viable CD3⁺CAR⁺ cells were sorted from cryopreserved CAR-T cell products or peripheral blood mononuclear cells (PBMC). Next, libraries were generated with the 10x Genomics Chromium single-cell 3′ platform with feature barcoding technology to allow simultaneous and paired quantification of transcriptional and cell-surface protein expression in individual CAR-T cells (19). The inclusion of feature barcoding in addition to enabling assessments of key markers at the protein level also allowed the discrimination of the memory markers CD45RA and CD45RO that cannot be discriminated at the RNA level. The libraries were sequenced, and the data were stringently filtered to remove, on average, 8.1% mitochondrial reads and yielded 94,000 cells with an average of 3,917 cells per sample, 8,518 reads per cell, and 2,263 unique detectable genes per cell (Supplementary Table S3). Batch effect removal was applied to remove differences due to sample preparation or sequencing.

To appreciate the heterogeneity present in the CAR-T cells among both patients and time points, RNA-based dimension reduction and unbiased clustering were applied to the scRNA-seq data set to yield 19 clusters with distinct transcriptional profiles (Fig. 1B and C). At preinfusion, a consistent clustering pattern was observed across patients, with the majority of preinfusion cells in clusters 0 to 4. After infusion, heterogeneity changed across patients, with additional clusters identified as well as an increased distribution of cells across clusters. For example, at day 14, the proportion of cells in clusters 5 to 10 increased as compared with preinfusion, whereas the frequency of cells in clusters 2 and 4 remained the same. From days 14 to 30, the distribution of cells across clusters decreases, with four clusters (C0, C2, C5, and C9) becoming dominant. Thus, we see an increase in heterogeneity followed by a loss by day 30 (Supplementary Fig. S1A). In accordance with peak expansion expected around day 14, clusters primarily composed of preinfusion and day 14 samples (C1, C4, and C6) contained the most actively dividing cells as evidenced by their abundance in the S phase by cell-cycle analysis (Fig. 1B; Supplementary Fig. S1B). The differential gene expression of individual clusters compared with other clusters demonstrated that the cells belonging to these clusters were highly enriched (P_adj < 0.05) in genes associated with immature T-cell types and proliferation such as MKI67, PCNA, TYMS, and TOP2A (Fig. 1C; ref. 20). In contrast, other major clusters (C2, C5, and C8) that consist predominantly of CD8 CAR-T cells from the day 14 and day 30 samples were found not to be actively proliferating and exhibited higher expression (P_adj < 0.05) of genes associated with effector CD8 T-cell phenotypes including GZMB, GZMH, GZMK, PRF1, GNLY, CCL4, and CCL5 (Fig. 1C). To investigate if the heterogeneity across clusters correlated with particular T-cell subtypes, SingleR cell ID annotation was applied. SingleR utilizes predefined T-cell subtype gene signatures to assign a CD4 or CD8 T-cell subtype to each cell, including naïve, central memory (CM), effector memory (EM), and terminal effector (TE) CD8 T-cell subtypes (21, 22). Application of cell identification results to the dimension reduction plot showed distinct localization of CD4 and CD8 T cells. Although the predominant CD8 subtype was EM, two clusters were enriched with CM transcriptional profiles (C0 and C2), and three clusters were enriched with TE transcriptional profiles (C5, C9, and C10; Supplementary Fig. S1C). Overall, CAR-T cells demonstrated significant heterogeneity across time points (product, day 14, and day 30), cell-cycle phase, cell type, and patient.

In CAR-T cell therapy, the expansion of cytotoxic CD8 CAR-T cells is thought to drive clinical responses through direct tumor lysis. In line with this notion, we hypothesized that we would observe evidence of greater cytotoxic T-cell to helper T-cell expansion as well as greater differentiation of postinfusion CD8 CAR-T cells compared with preinfusion CD8 CAR-T cells. Accordingly, we observed that the relative proportion of CD8 to CD4 CAR-T cells significantly increased after infusion from 52% to 87% CD8 CAR-T cells on average (P = 0.0005; Fig. 2A). The most significantly upregulated genes in postinfusion CD8 CAR-T cells from merged day 14 and day 30 samples included transcription factors (PRDM1 and EOMES) and cytotoxic effector molecules (GZMB, PRF1, GZMK, and CCL5) associated with differentiation into cytotoxic effector cells (DESeq2; P_adj < 0.05; Fig. 2B). Notably, transcription factors associated with exhaustion (TOX, TOX2, NR4A2, and NR4A3) were also significantly upregulated postinfusion, and gene set enrichment analysis of memory/effector versus exhaustion gene sets showed that postinfusion CD8 CAR-T cells were significantly enriched in exhaustion-related genes compared with preinfusion (P < 0.05; Supplementary Fig. S2A). Notably, an upregulated expression of the exhaustion markers CTLA4, LAG3, HAVCR2 (TIM3), PDCD1 (PD-1), VSIR (VISTA), and TIGIT in merged day 14 and 30 postinfusion samples compared with the product was observed in both CD4 and CD8 CAR-T cells, with TIGIT being the most significant for both cell types [pre- vs. postinfusion CD8 CAR-T; average log₂ fold change (FC) = 2.39; P < 0.0001; Fig. 2C; Supplementary Fig. S2B and S2C]. Quantification of cell-type proportions across time points using SingleR transcription-based cell assignments indicated a shift from an equal proportion of CD8 CM and EM profiles in the product toward an EM profile at day 14 and a combination of EM and TE profiles at day 30 (Fig. 2D). Altogether, gene-expression analyses indicate that CD8 CAR-T cells undergo differentiation toward a cytotoxic effector profile and exhaustion after infusion into patients.

Given the changes in the transcriptional profile of CD8 CAR-T cells postinfusion, changes in cell-surface phenotype upon infusion were expected to reflect differentiation toward EM or terminally differentiated phenotypes. We previously demonstrated that CD8 CAR-T cell products are enriched in an early memory phenotype with the expression of CD45RA, CCR7, and TCF7 (23). Here, feature barcoding markers were chosen for their association with stemness/early memory [CD45RA, CD197 (CCR7), CD127 (IL7R), CD62L, CD25, CD28], activation/EM [CD25, CD69, CD45RO, CD279 (PD-1)], terminal differentiation (CD45RA and CD57) and exhaustion (PD-1; refs. 24, 25). Consistent with the RNA expression data, we observed a greater cell-surface expression of the naïve and early memory markers CD45RA, CD127, CD62L, and CD25 in the product (Fig. 2E and F). By day 14, a global increase in CD45RO, CD69, CD57, and PD-1 (CD279) was observed, suggesting differentiation toward an activated, effector-like state (Fig. 2E and F). However, at the cluster level, clusters 5, 9, and 10 were found to be enriched in CD45RA and CD57, indicative of cluster-specific enrichment with a terminally differentiated phenotype. We next compared changes from days 14 to 30 for evidence of further differentiation and observed an additional increase in global CD45RA and CD57 expression. There was also a global increase in the memory markers CD127, CD25, and CD197 from days 14 to 30, consistent with the contraction of effector cells by day 30 after tumor resolution. Overall, the cell-surface phenotypes of CD8 CAR-T cells both pre- and postinfusion as measured by feature barcoding showed high similarity to the RNA expression results, indicating CD8 T-cell differentiation to EM and TE phenotypes.

Next, flow cytometry was utilized to validate changes in memory status and exhaustion marker expression in CD8 CAR-T cells with additional patient samples (Supplementary Table S2). Similar to the previous analysis using surface protein feature barcoding, we observed an increased proportion of CD45RA⁺CCR7⁺ cells in the product (average 29%) as compared with postinfusion samples (average 3.6%), indicative of a greater proportion of early memory T cells (Fig. 2G). At day 14, an average 83.3% of CD8 CAR-T cells were CD45RA⁻, and the predominant phenotype (average 66.7%) was CD45RA⁻CCR7⁻CD27⁺, a significant shift toward an effector phenotype as compared with CAR-T cells in the product (P < 0.0001; Fig. 2H). Although day 30 samples also contained high frequencies of CD45RA⁻CCR7⁻CD27⁺ cells, there was a significant increase in CD45RA⁺CCR7⁻ cells among CD8 CAR-T cells, once again indicative of terminal differentiation (P < 0.01; Fig. 2I; Supplementary Fig. S2D). Notably, at both day 14 and day 30, CD8 CAR-T cells could be distinguished from endogenous CD8 T cells by flow cytometry by expression of CD27, which may contribute to the long-term maintenance observed in 4-1BB.CAR-T cells (Supplementary Fig. S2E; ref. 26). Altogether, the data support a shift from early memory to EM and TE CD8 CAR-T cells in postinfusion samples.

As gene expression and feature barcoding data sets indicated higher levels of exhaustion marker expression postinfusion, we further assessed changes in checkpoint molecules by flow cytometry. In the manufactured CAR-T cell product, 68% of CD8 CAR-T cells expressed TIM3. Although this could be indicative of early exhaustion or senescence, this expression was attributed to activation during CAR-T cell manufacture, as expression dropped after infusion to an average 24% TIM3⁺ (Fig. 2J). Due to the decrease in TIM3 protein expression postinfusion, it was not considered a dominant mediator of CAR-T cell exhaustion in our patients. Among the exhaustion markers assayed, PD-1 and TIGIT expression was significantly induced to an average 39.1% PD-1⁺ and 38.7% TIGIT⁺ CD8 CAR-T cells at day 14 (P < 0.05; Fig. 2J). TIGIT expression was the most sustained at day 30 with an average 34.8% TIGIT⁺ CD8 CAR-T cells. In addition, among the exhaustion markers assayed, PD-1 and TIGIT were also the most upregulated on CD4 CAR-T cells, increasing to an average 50.4% PD-1⁺ and 42.9% TIGIT⁺ postinfusion (days 14 and 30; Supplementary Fig. S2F). To determine if the observed expression of PD-1 and TIGIT could be attributed to CAR signaling, we compared the expression of exhaustion markers in CAR-T cells to endogenous T cells. Both CD4 and CD8 CAR-T cells showed the enrichment of TIGIT compared with endogenous CD4 and CD8 T cells, with an average 9.5% and 10.4% higher expression of TIGIT on CD8 CAR-T cells at days 14 and 30, respectively (Supplementary Fig. S2G and S2H).

As there are a variety of different CD19 CAR-T products used clinically, we next assessed TIGIT and PD-1 expression after infusion on T cells from patients receiving three different FDA-approved CD19 CAR-T cell products (Yescarta, Kymriah, and Tescartus). Similar to the CAR-T product used for the previous studies, we observed high expression of TIGIT and PD-1 on CD4 and CD8 CAR-T cells with an average 65.6% and 53.4% CD8 CAR-T cells expressing TIGIT and PD-1, respectively (Supplementary Fig. S2I–S2K). Once again, endogenous T cells also expressed TIGIT, though lower than CAR-expressing cells (Supplementary Fig. S2I and S2K). Altogether, our data indicate that there is elevated expression of TIGIT on CD4 and CD8 CAR-T cells postinfusion, and TIGIT expression in these cells is strengthened by CAR expression. Though PD-1 in addition to TIGIT was also found to be consistently induced postinfusion at both the RNA and protein levels, we focused further studies on TIGIT, as it is comparatively less studied in this context and TIGIT expression exhibited more marked and durable changes.

With the evidence of exhaustion in postinfusion CAR-T cells, we next looked for differences in the exhaustion profile in CAR-T cells from favorable and poor responders. We found that CD8 CAR-T cells of poor responders exhibited significantly decreased expansion and persistence compared with responders by flow cytometry (P < 0.05; Fig. 3A). A similar but not statistically significant result was observed for CD4 CAR-T cells (Supplementary Fig. S3A). At the RNA cluster level, cell frequencies across responder groups were comparable (Supplementary Fig. S1A; Fig. 3B). Across assigned CD8 T-cell subtypes, CD8 CAR-T cells of poor responders contained a significantly greater proportion of EM cells, likely indicative of greater CAR signaling in proportion and magnitude in poor responders (P = 0.044; Supplementary Fig. S3B). We next compared differentially expressed genes of postinfusion CD8 CAR-T cells between poor and favorable response groups to identify dysregulated genes in the CD8 CAR-T cells of poor responders. Of note, significantly upregulated transcription factors included FOS, JUNB, JUND, FOSB, JUN, NR4A2, NFKBIA, and PRDM1 (MAST; P_adj < 0.0001; Fig. 3C). To confirm a dysfunctional profile in postinfusion CD8 CAR-T cells from patients with poor response, we performed dysfunction scoring by applying three different exhaustion signatures (1, 27, 28). Consistently, we observed significantly higher dysfunctional scores in CD8 CAR-T cells of poor responders both globally (P < 0.0001) and within the most predominant clusters (Fig. 3D). Higher dysfunction scores were also consistent across CD8 CAR-T cell subtypes (Supplementary Fig. S3C). Upon direct investigation of exhaustion marker expression within individual patients, the upregulation of TIGIT was most prominently observed in the CD8 CAR-T cells of poor responders (logFC = 2.8) as compared with responders (logFC = 2.25; Fig. 3E). This was also even more evident in the percentage of CD8 CAR-T cells expressing TIGIT at the RNA level, where we observed ∼62% (range, 56%–67%) of postinfusion CD8 CAR-T cells of poor responders expressed TIGIT compared with ∼27% (range, 12%–37%) of cells from responders (Fig. 3E).

Consistent with the transcriptional analysis of TIGIT by scRNA-seq, the measurement of exhaustion marker expression by flow cytometry showed exhaustion markers were consistently expressed at higher levels in postinfusion poor responder CD8 CAR-T cells compared with the cells of responders (Fig. 3F–H). Among the exhaustion markers, TIGIT was once again the most prominent, with a greater than 20% increase in the average percentage of TIGIT⁺ CD8 CAR-T cells in the postinfusion poor responder samples (Fig. 3F–H). A similar but nonsignificant trend was also observed in CD4 CAR-T cells (Supplementary Fig. S3D). Interestingly, a comparison of TIGIT expression between endogenous CAR⁻ CD8 T cells of poor and favorable response groups showed a similar trend at day 14 (P = 0.11) and day 30 (P = 0.17), with higher TIGIT expression in poor response groups (Supplementary Fig. S3E). Consistent differences in the protein expression of memory markers CD45RA, CCR7, and CD27 between CD8 CAR-T cells of response groups were not apparent (Supplementary Fig. S3F). As elevated CAR expression can induce antigen-independent signaling and accelerate CAR-T cell differentiation, exhaustion, and apoptosis, we compared CAR expression in the CD8 CAR-T cells of products across response groups to assess if this alternative mechanism to antigen-dependent signaling could contribute to our findings (29). Indeed, we observed a trend of greater CAR expression in CD8 CAR-T cells of poor responders compared with responders (Fig. 3I). Overall, our data support that CD19 CAR-T cells of poor responders in our trial have enrichment in differentiated and exhausted CD8 CAR-T cells, and TIGIT is a novel prognostic marker of response.

TIGIT expression has been associated with a dysfunctional T-cell phenotype in chronic infection and cancer, but its role in CAR-T cell dysfunction has not been explored (30–32). As our previous flow cytometry analyses revealed, TIGIT was the most widely expressed checkpoint receptor in postinfusion CD8 CAR-T cells, particularly in poor responders, we assessed if TIGIT can serve as a marker of CAR-T cell dysfunction. To address this question, we separated TIGIT⁺ and TIGIT⁻ CD8 CAR-T cells in silico and performed differential gene expression. Similar to the profile observed comparing favorable and poor responders, total TIGIT⁺ cells overexpressed genes associated with exhaustion (Fig. 4A). In preinfusion TIGIT⁺ cells, upregulated genes included PDCD1 (PD-1), LAG3, EOMES, and PRDM1, and downregulated genes included TCF7, SELL, and CCR7 (P_adj < 0.05), corresponding with increased differentiation and decreased stemness. Postinfusion, TIGIT⁺ CAR-T cells had elevated levels of exhaustion markers including TOX, PDCD1, and GZMK. Elevated TIGIT expression was not associated with any particular cluster or cell subtype in day 14 and day 30 postinfusion CD8 CAR-T cells (Fig. 4B; Supplementary Fig. S4A and S4B). We next applied T-cell dysfunction scoring to compare TIGIT⁺ cells with TIGIT⁻ cells. TIGIT⁺ cells had higher dysfunction scores globally (P < 0.0001) and in the most predominant clusters across the three gene sets tested (Fig. 4C). Furthermore, high dysfunction scores colocalized with TIGIT expression (Fig. 4B and C). Accordingly, upon a comparison of exhaustion marker protein expression in TIGIT⁺ versus TIGIT⁻ CD8 CAR-T cells across all three time points, we observed significantly increased expression of CTLA4, LAG3, and PD-1, with average fold changes of 1.94, 1.95, and 1.48, respectively (P < 0.05; Fig. 4D–F). The same trend was observed in the CD4 CAR-T cells as well as the endogenous CD8 T cells of patients, with consistent positive fold increases for most exhaustion markers across patients and time points between TIGIT⁺ and TIGIT⁻ T cells (Supplementary Fig. S4C and S4D). Altogether, this evidence supports TIGIT as a marker of dysfunction in the context of CAR-T cell therapy and NHL.

TIGIT is an immunoregulatory protein thought to inhibit T cells and natural killer (NK) cells by competing with the costimulatory receptor DNAM-1 (gene name CD226) in binding to their common ligands PVR and PVRL2 (33, 34). Studies also suggest that TIGIT can bind to DNAM-1 in cis, disrupting DNAM-1 homodimerization, and it may directly inhibit T-cell activation by signaling through its inhibitory ITT and ITIM domains (32, 35). In NHL, PVR expression has been reported on tumor cells and endothelial cells (30); other contexts have additionally reported expression on intratumoral myeloid cells (36, 37). This pathway is thought to be important in immune evasion, with complex regulation of its components. For example, PVR is often expressed at higher levels in human malignancies while displaying protumorigenic properties, making it an attractive therapeutic target (38). Furthermore, in T-cell lymphoma, DNAM-1 surface expression on circulating endogenous CD8 T cells is diminished compared with healthy controls, whereas serum levels of DNAM-1 are elevated, indicating shedding of its membrane form and the potential role of this pathway as an immune escape mechanism (39). However, in the same context, it was also observed that recombinant DNAM-1 could induce cytotoxicity on PVR-expressing tumor cells (39). Together, this highlights the need to determine if TIGIT competition with DNAM-1 for PVR binding is a predominant mechanism of CAR-T cell dysfunction. To test this, we first measured DNAM-1 expression on CAR-T cells from the clinical product and after infusion in patients. In the product, CAR-T cells express high protein levels of DNAM-1 in both CD4 and CD8 subtypes with an average 97.7% and 99.2% DNAM-1⁺, respectively (Supplementary Fig. S5A; Fig. 5A). The protein expression of DNAM-1 remains high postinfusion at days 14 and 30 in CD4 and CD8 CAR-T cells with an average 77.6% and 84.4% of CD4 CAR-T cells and 60% and 80% of CD8 CAR-T cells expressing DNAM-1, respectively. We next determined if TIGIT and DNAM-1 were coexpressed, and observed DNAM-1 expression at days 14 and 30 after infusion on the majority of TIGIT⁺ CD4 (76%; 83%) and CD8 (67%; 80%) CAR-T cells and endogenous CD4 (84%; 86%) and CD8 (68%; 86%) T cells, indicating TIGIT competes with DNAM-1 for binding their common ligand PVR (Fig. 5B; Supplementary Fig. S5B). The percentage coexpression of TIGIT and DNAM-1 is slightly elevated in patient responders due to higher levels of TIGIT expression (Fig. 5C; Supplementary Fig. S5C). These data indicated that TIGIT blockade in combination with CAR-T cell therapy may be a means to improve CAR-T cell therapy.

To test the effect of TIGIT blockade on CAR-T cell function and recapitulate the CAR-T cell exhaustion we observed in patients, we utilized an in vitro chronic stimulation model. In this model, clinical CAR-T products used to treat patients with NHL were stimulated with CD19⁺ Raji lymphoma cells every three days at a 4:1 ratio of CAR-T cells to Raji cells. Aliquots of cells were analyzed by flow cytometry prior to stimulation and at intervals following coculture. In this model, CAR-T cells proliferated robustly until day 12 of culture whereupon they stopped proliferating and exhibited a failure to kill the Raji cells after stimulation, indicative of reaching an exhausted state (Supplementary Fig. S5D). When comparing the proportions of CD4 and CD8 CAR-T cells, an increase in the CD8/CD4 cell ratio was observed similar to the trend found in patients (Supplementary Fig. S5E). To further validate an exhausted profile, we next compared the expression of the exhaustion markers CTLA4, LAG3, PD-1, TIGIT, TIM3, and VISTA across time points. Akin to the clinical setting, we observed increased expression of the inhibitory receptors on CD4 and CD8 CAR-T cells, with a peak at 37% TIGIT⁺ for CD4 CAR-T cells and TIGIT expression increasing from 17% prestimulation to 54% at day 15 on CD8 CAR-T cells (Supplementary Fig. S5F–S5H).

To evaluate the effect of TIGIT blockade in this in vitro exhaustion model, we first transduced Raji cells with the TIGIT ligand PVR, as Raji cells do not express detectable PVR. We then utilized the same model with a TIGIT-blocking antibody or IgG control. On day 9 after the CAR-T cells were pulsed with the fourth stimulation of Raji-PVR, we assessed the ability of the CAR-T cells to kill the Raji-PVR cells with or without TIGIT blockade. We observed an average of 30% increase in cytotoxicity with TIGIT blockade as compared with the IgG control at this time point (Fig. 5D). After the next stimulation of CAR-T cells with Raij-PVR on day 12, the CAR-T antitumor function was further assessed by staining for the cytokines IFNγ and TNFα, cytotoxic molecules granzyme B and perforin, and the proliferation marker Ki-67. TIGIT blockade led to a fold increase in the median fluorescent intensity of TNFα, granzyme B, and perforin of 1.52, 4.56, and 5.05, respectively, and a 15% increase in Ki-67⁺ proliferating CD8 CAR-T cells as compared with the IgG control (Fig. 5E–G). In CD4 CAR-T cells, a 1.42-fold increase in TNFα expression was observed (Supplementary Fig. S5I). As with the CAR-T primary patient samples, TIGIT and DNAM-1 coexpression was preserved in this model, suggesting TIGIT competition with DNAM-1 for PVR binding as a possible mechanism for improvement in CAR-T cell antitumor function (Supplementary Fig. S5J). Overall, we were able to recapitulate TIGIT induction in CAR-T cells using a chronic stimulation model and demonstrate that TIGIT blockade may enhance the antitumor function of the CAR-T cells.

We next evaluated the effect of TIGIT blockade in vivo using a mouse model of human NHL. As CD19 CAR-T cells alone have significant efficacy in traditional mouse NHL models, we first used a high tumor burden model where the CAR-T cells alone have significantly less efficacy to assess if TIGIT antibody could improve overall survival. Mice were injected intravenously (i.v.) with luciferase-expressing Raji-PVR lymphoma cells, and treatment was initiated after three weeks with CAR-T cells and weekly injections of TIGIT blocking antibody or IgG control. Importantly, the combination of TIGIT blockade and CAR-T cell therapy led to a significant survival advantage over CAR-T cell treatment alone (P = 0.0003), with 33% of the mice surviving long term (Fig. 5H). Reduced tumor growth was also observed as measured by bioluminescence imaging (Fig. 5I). TIGIT antibody blockade as a single-agent therapy had no impact on tumor burden or survival in the absence of human CAR-T cells, likely due to the absence of TIGIT expression on Raji cells and the use of a human-specific antibody (Supplementary Fig. S6A–S6C). To further evaluate the effect of TIGIT blockade we used a conventional tumor model with CAR-T cell treatment initiated one week after Raji-PVR cell injection. Once again, TIGIT blockade led to superior control of tumor growth with near-complete elimination of tumor burden, in contrast to mice treated with CAR-T cells and control IgG (Supplementary Fig. S6D). Whereas the CAR-T cells of mice without TIGIT blockade showed a dysfunctional profile in the blood, spleen, and bone marrow, mice with TIGIT blockade displayed low inhibitory checkpoint receptor expression suggestive of tumor clearance and memory formation (Supplementary Fig. S6E). Similar to our observations from postinfusion CAR-T cells of patient samples and the chronic in vitro exhaustion study, high TIGIT expression was observed in the circulating CAR-T cells in the mice treated with control IgG antibody at two weeks after CAR-T cell injection and was highly coexpressed with DNAM-1 in both in vivo models used (Fig. 5J; Supplementary Fig. S6F).

Due to the variability in outcomes of patients with NHL receiving CD19 CAR-T therapy, it is important to better understand the changes CAR-T cells undergo after infusion in the patient and to compare profiles among patients with favorable and poor outcomes. Here, we compared differences in both transcriptional and phenotypic profiles of purified CAR-T cells between time points and response groups. We show that CAR-T cells isolated from the blood of patients with NHL exhibit marked changes after infusion toward transcriptional and surface marker profiles of highly activated and differentiated T cells (18). Both memory marker and gene-expression cell identification demonstrate heterogeneity in the memory phenotypes of postinfusion CD8 CAR-T cells, including CM, EM, and TE cell types. Overall, we observed a shift in the predominant profiles of CD8 CAR-T cells from CD45RA^hiCCR7^hiCD127^hiCD62L^hiCD25^hi to CD45RO^hi­CD28^hiCD69^hiCD27^hiPD-1^hi and CD45RA^hiCD57^hiCD69^hi­PD-1^hi upon infusion, supporting a unique phenotype for CAR-T cell differentiation. Overall, we observed CAR-T cells of poor responders had a more differentiated phenotype, suggesting greater antigen exposure consistent with progressive disease and less proliferative capacity.

We also noted the upregulation after infusion of several transcription factors implicated in driving T-cell exhaustion (TOX, NR4A2, EOMES, and PRDM1) as well as inhibitory checkpoint receptors associated with exhaustion (TIGIT and PD-1). Although the induction of CTLA4, TIM3, and LAG3 RNA expression was observed at the RNA level, protein expression was not apparent by flow cytometry, providing a further rationale to pursue an investigation of the expressed inhibitory checkpoint receptor TIGIT. A comparison of antigen-exposed CAR-T cells with non–CAR-expressing T cells from postinfusion samples shows similar evidence of exhaustion in CAR-T cells, with significantly higher levels of expression of TIM3 and TIGIT in CD8 CAR-T cells. Together, this supports the hypothesis that chronic stimulation drives exhaustion in postinfusion CD8 CAR-T cells. Of note, CD27 in particular distinguished CD8 CAR-T cells from non–CAR-expressing CD8 T cells, making it an additional marker for the identification of 4-1BB CAR-T cells in future applications.

We further demonstrate that CD8 CAR-T cells from poor responding patients with PD or SD are enriched with an exhaustion profile as compared with favorable outcome patients. Among the panel of exhaustion markers tested, TIGIT was found to be the most differentially expressed among response groups. Differential gene expression between postinfusion CD8 CAR-T cells of response groups revealed overexpression of transcription factors including PRDM1, NR4A2, NFKBIA, and AP-1 family members in poor responders, many of which have been implicated in driving T-cell exhaustion (40, 41). In particular, the RNA expression of AP-1 members FOS, JUN, JUNB, JUND, and FOSB was greater in CD8 CAR-T cells of poor responders. Although historically AP-1 family member expression has been associated with greater T cell–mediated antitumor function, with the absence of AP-1 allowing exhaustion driven by NFAT, our results show high AP-1 expression postinfusion, indicating this is not a mechanism of exhaustion in our system. Similar to our findings, Lynn and colleagues demonstrate that AP-1 family members JUN, JUNB, and FOSB are overexpressed, and all AP-1 family member binding motifs are significantly enriched in exhausted compared with functional CD28.CAR-T cells (40). Lynn and colleagues proceed to evaluate the protein expression of AP-1 family members and only then are able to hypothesize a relative deficiency of c-Jun/c-Fos heterodimers and identify c-Jun overexpression as a means of providing exhaustion resistance in CAR-T cells. Thus, although the comparisons made here and by Lynn and colleagues are distinct, we nonetheless observe a similar pattern of RNA expression. Further studies would be required to investigate the role of AP-1 transcription factors in this context.

The differential expression of TIGIT between response groups was particularly notable given the evidence of exhaustion in CD8 CAR-T cells from poor or favorable response groups and suggested that TIGIT may be a more suitable marker for severe exhaustion at least for 4-1BB.CAR-T cells compared with commonly associated molecules such as PD-1 or TOX. Therefore, we surveyed the phenotype of TIGIT⁺ cells to determine its relevance as a biomarker or driver of response. CD8 TIGIT⁺ CAR-T cells had greater dysfunctional scores compared with TIGIT⁻ cells, upregulated TOX as well as many of the same exhausted-related genes differentially expressed between response groups, and had a higher surface expression of all exhaustion markers tested, including PD-1. Endogenous TIGIT⁺ CD8 T cells also had increased exhaustion marker expression, suggesting activity of this immunoregulatory pathway in NHL independent of CAR-T cell presence. Mechanistically, TIGIT likely drives CAR-T cell dysfunction by competing with DNAM-1 for binding the ligands PVR and PVRL2 expressed on the endothelium, surrounding immune cells, and tumor tissue (30).

Despite the promising efficacy of CD19 CAR-T therapy for NHL, a significant number of patients fail to achieve remission or exhibit early relapse. As blockade of the checkpoint inhibitor PD-1 on T cells has received intense attention in cancer therapy, several groups have assessed the potential for PD-1/PD-L1 blockade to improve CAR-T efficacy for NHL as well as other malignancies (42–48). Although investigations are still ongoing, to date there is no strong evidence that impairing the PD-1/PD-L1 axis alone leads to significantly improved efficacy of CD19 CAR-T therapy. In fact, in some cases the early disruption of PD-1 has been found to negatively affect CAR-T cell activity, suggesting that its potential benefit may be at later time points as compared with TIGIT abrogation in our model systems (42). More recently, efforts have focused on combination approaches to abrogate multiple checkpoint molecules, as individual molecules may have specialized functions (49). For example, it was recently reported that the expression of shRNAs to simultaneously downregulate TIGIT and PD-1 expression can enhance CAR-T activity in a mouse tumor model; however, our findings are the first to demonstrate the improvement of CAR-T efficacy in mouse models with TIGIT inhibition alone as well as with a clinically relevant monoclonal blocking antibody (50).

Though the marked upregulation of TIGIT on CD19 CAR-T cells and the efficacy of the combination of CD19 CAR-T cells with a clinically used TIGIT blocking antibody have not been previously reported, TIGIT has been previously explored as an important checkpoint molecule in cancer through both preclinical and clinical studies (32, 51). To date, the results have been somewhat conflicting, possibly due to differences in the importance of TIGIT based upon the specific context and tumor type. For example, Wen and colleagues recently reported that TIGIT expression in a large pan-cancer gene-expression study correlates with exhausted T cells (52). However, the correlation of TIGIT expression with patient outcome was highly dependent on tumor type, and no difference was observed in this study for diffuse large B-cell lymphoma. Meanwhile, Yang and colleagues found that there was an increase in TIGIT-positive T cells in patients with follicular lymphoma who had poor outcomes (53). In addition, despite the fact that we observe increased efficacy of TIGIT blockade combined with CD19 CAR-T cells in our lymphoma model systems, others have observed that TIGIT primarily suppresses antitumor immunity via T regulatory cells (54). Furthermore, it is possible that similar results will not be found with CD19-based antibody therapies. For example, Roider and colleagues did not observe any correlation of responses to a bispecific CD3/CD19 antibody with TIGIT expression in preclinical models (55). In addition to our approach to utilize a therapeutic monoclonal TIGIT blocking antibody, Hoogi and colleagues have engineered T cells to express a chimeric switch receptor that fuses the TIGIT extracellular domain to an activating signal and leads to enhanced T-cell efficacy in preclinical models (56). Beyond T cells, TIGIT blockade has also been shown to prevent NK cell exhaustion and improve antitumor function (57).

There are several noteworthy considerations to review in the current study. First, a limited number of poor responder samples were available at the time of analysis. To offset this limitation, a large number of cells (94,000) were profiled by scRNA-seq and combined with protein measurements by feature barcoding. Furthermore, additional poor responder samples were incorporated in a flow-cytometric validation data set. Likewise, pseudobulk comparisons, flow cytometry, and functional analyses were used to offset the limitations that exist with scRNA-seq analyses, such as a tendency for false discoveries (58). Another consideration is a focus on CAR-T cells derived from the blood in contrast to tumor tissue. Although it is likely that tumor-derived samples could offer great insight into relevant transcriptional profiles and phenotypes of poor responder samples, this would prohibit a comparison with postinfusion favorable response samples, and it is preferable to seek relevant biomarkers from minimally invasive samples. Additionally, although we have demonstrated TIGIT to be a biomarker of response with potential functional relevance, future research will need to be conducted to assess the loss of CAR-T cell functionality in patients over time and between response groups as well as the clinical potential of this approach for CAR-T therapy. Although we demonstrate that TIGIT is expressed at a high level after infusion of FDA-approved products including CD28.CAR-T cells (Kymriah and Tescartus), more studies are also needed to determine the functional effect of TIGIT blockade with designs and manufacturing methods besides our own. It would further be beneficial to include epigenetic studies of CAR-T cells used in the treatment of NHL to strengthen the existence of exhaustion in CAR-T cells and relate it to T-cell exhaustion without CAR expression.

For both the investigational and validation cohorts, all patients failed at least two previous lines of therapy and were enrolled to the study in accordance with eligibility criteria described in clinicaltrials.gov (Identifier: NCT03434769; IND 17932). Leukapheresis products for the generation of CAR-T cell products were obtained from patients with NHL receiving CAR-T cell therapy at University Hospitals Seidman Cancer Center under a phase I/II study and utilized within 24 hours of draw. All blood samples were processed via density gradient centrifugation and were cryopreserved in the University Hospitals Biorepository and Cellular Therapy facility. The study was approved by the institutional review board, and all patients gave written informed consent. Available clinical information for the cohorts is given in Supplementary Table S2. The analysis of the validation cohort is limited to Supplementary Fig. S2I and S2J.

CAR-T cell manufacture was automated with the use of the CliniMACS Prodigy device using the TCT software program and the TS520 tubing set (Miltenyi Biotec). The instrument setup and technical protocol were described by Zhu and colleagues (59). The clinical-grade reagents applied in this process were CliniMACS Buffer, TexMACS Media, CliniMACS CD4 reagent, CliniMACS CD8 reagent, TransAct, and the cytokines IL7 and IL15 (Miltenyi Biotec). Peripheral blood apheresis products were loaded into the machine, and CD4 and CD8 T cells were isolated using CliniMACS CD4 reagent and CliniMACS CD8 reagent according to the manufacturer's instructions. The isolated T cells were then stimulated with IL7 and IL15 (Miltenyi Biotec) at a concentration of 25 μg/2L bag of TexMACS media with 3% human AB serum (Innovative Research). Human AB serum was removed after the sixth day of culture. The viability, purity, and potency of the products were confirmed as previously described (23). This process was performed at the Cellular Therapy Lab of University Hospitals Cleveland Medical Center Seidman Cancer Center/Case Western Reserve University Center for Regenerative Medicine.

The 4-1BB.CAR construct applied in the clinical trial was developed by Lentigen, a Miltenyi Biotec company. The vector is composed of the FMC63 scFv, a CD8-derived hinge region, the TNFRSF19-derived transmembrane domain, the CD3ζ intracellular domain, and the 4-1BB costimulatory domain.

Cryopreserved apheresis products were thawed and prelabeled with antibodies for flow cytometry (described below) and a panel of TotalSeq-B antibodies (described below) from BioLegend. The panel of TotalSeq-B antibodies included CD127, CD197, CD25, CD279, CD28, CD4, CD45RA, CD45RO, CD57, CD62L, CD69, and CD8. Information on the utilized TotalSeq-B antibodies is given in Supplementary Table S4. Live CD3⁺CAR⁺ T cells were sorted by FACS, and preparation of single-cell and TotalSeq-B libraries was performed utilizing the 10x Genomics Chromium Single-Cell 3′ Reagent Kits with feature barcoding technology for cell-surface protein (v3) according to the manufacturer's instructions. Libraries were sequenced by Psomagen, Inc.

A total of 27 CAR-T samples were sequenced. For each sequenced scRNA-seq pool, Cell Ranger (v3.1.0) from 10× Genomics was used to process, align, and summarize unique molecular identifier (UMI) counts against hg38 human reference genome. For 16 of the 27 CAR-T samples, 12 T-cell surface proteins (CD28, CD57, CD69, CD62L, CD197, CD25, CD279, CD45RA, CD127, CD4, CD45RO, and CD8A) were barcoded to each cell by TotalSeq-B antibody library (BioLegend). Based on each sample mRNA assay UMI metrics, cells with too low or high UMI counts or the number of genes (i.e., 2.5 × standard deviation) were filtered out. Cells with a mitochondrial UMI count proportion higher than 15% were removed. Cells with a ratio of the number of genes covered to UMI counts less than 0.1 were also filtered. Doublets, as annotated by Scrublet v.0.2.1 (60), were also removed. After the comprehensive quality control procedure, we retained only 24 CAR-T samples, because for three samples (patient 5 day 14, patient 5 day 30, and patient 12 day 30) either the number of read counts or the number of genes was too small compared with the other samples. See post-quality control metrics in Supplementary Table S3.

CAR-T cell subtypes were assigned by SingleR (ref. 21; v1.0.6). Gene-expression profile was compared with the RNA-seq transcriptome profile of 29 immune cell types (22). The best-predicted cell type was considered. CAR-T cells were grouped into CD4 and CD8 T cells. CD4⁺ T cells were annotated as T follicular helper cells, regulatory T cells, Th1, Th1/Th17, Th17, Th2, naïve, or TE. CD8⁺ T cells were annotated as naïve, CM, EM, or TE.

The 24 CAR-T samples available post–quality control (see above) from 13 patients include a total of 94,000 high-quality cells with an average of 3,917 cells per sample originally sequenced from 3 batch sequencing runs. The cells were obtained from the quality control step described above (in the section titled “Quality Control of Raw 10x scRNA-seq Data”). Individual patient samples were merged together. Seurat R package (ref. 61; v3.2.3) was used to log-normalize expression values for total UMI counts per cell via Seurat::NormalizeData (normalization.method = “LogNormalize,” scale.factor = 10,000). Then, 2,000 highly variable genes were identified by fitting the mean–variance relationship via Seurat::FindVariableFeatures (selection.method = “vst”). Cell cycle was inferred by a function, Seurat::CellCycleScoring, from the Seurat package using 43 genes for the S state and 54 genes for the G₂–M state, respectively. For batch effect removal, low dimension visualization, and clustering procedure, we first regress out the two continuous variables, (i) mitochondrial gene expression and (ii) cell-cycle annotation via Seurat::ScaleData(). The merged samples batch effect signal was corrected using the Harmony (v1.0) algorithm (62), with two categorical variables via RunHarmony(group.by.vars = c(“samples,”“seq.run”)). The aligned cells were then clustered using the Louvain algorithm for modularity optimization using the kNN (k nearest neighbors) graph as input with the first 50 principal components and Seurat::FindClusters (resolution = 0.8). Cell clusters were visualized using the tSNE algorithm (63) with a dimension reduction input from Harmony.

Given the read count matrix after the rigorous quality control procedure (see “Quality Control of Raw 10x scRNA-seq Data” above), we generated a pseudobulk mRNA read count matrix. In Figs. 2B and 3C, we summed the read counts across cells and computed the total gene counts in an individual sample. In Fig. 4A and B, we summed the read count by whether TIGIT is expressed or not in each sample and generated two pseudobulk mRNA count matrices. Then, DESeq2 (with all default parameters) was applied to these pseudobulk mRNA data sets in a couple of comparisons (e.g., preinfusion vs. postinfusion CAR-T, poor vs. favorable response, or TIGIT⁻ vs. TIGIT⁺). In a comparison of TIGIT expression in postinfusion with that of preinfusion in the same patient in the pseudobulk model (Fig. 3E), we randomly sampled cell bar codes to generate three replicates within the same sample, which is suggested in DESeq2 analysis. Statistical significance is indicated by one or more asterisks, and the number of asterisks shown corresponds to P values less than 0.05, 0.01, 0.001, or 0.0001, respectively.

For each identified cluster, we compared the cells within the clusters with all other cells using R packages Seurat and MAST (ref. 64; v1.16.0) for statistical testing to identify all marker genes expressed distinctly compared with the other clusters. Only differentially expressed genes of significance less than 5% FDR were retained. Without loss of generality, the same DEG testing is applied between the CAR-T product and postinfusion CAR-T cells, or between the cells belonging to the patients with a favorable or poor outcome within either the same cluster or T-cell subtype. The gene sets used for each panel are indicated in Supplementary Table S5. Statistical significance is indicated by one or more asterisks, and the number of asterisks shown corresponds to P values less than 0.05, 0.01, 0.001, or 0.0001, respectively.

We used gene set variation analysis (GSVA; v1.36.3; ref. 65) to compute gene set variance scores. To prepare the scRNA-seq data for GSVA, we randomly sampled cells from each time point into three replicates. Using the pseudobulk data, we then calculated gene set variance scores for each replicate. To assess differences in gene sets across time points, we used limma (v3.44.3; refs. 66–68) with the features from GSVA as input to lmFit with the default parameters. We then computed empirical Bayes statistics using eBayes prior to generating the top table. Volcano plots were produced with the EnhancedVolcano (v1.6.0) package.

In this study, we focused on a custom-made list of 106 genes for discussion and visualization. The genes are either directly or indirectly known to be associated with human immunology and are listed in Supplementary Table S6. The gene sets used for each panel are indicated in Supplementary Table S5.

CD8⁺ T-cell dysfunctional scores were calculated at each cell. We computed the AUC score of a gene set showing a CD8⁺ T-cell dysfunction phenotype using AUCell_calcAUC from R package AUCell (ref. 69; v1.8.0). Three signature gene sets are used for the prediction. The first gene set from Sade-Feldman and colleagues includes LAG3, PDCD1, HAVCR2, TIGIT, CD38, and ENTPD1 (27). The second gene set from van der Luen and colleagues includes a total of 22 genes: LAYN, ITGAE, PDCD1, CTLA4, HAVCR2, LAG3, TIGIT, CXCL13, CD38, ENTPD1, CDK1, HSPH1, CCCNB1, HSPB1, MKI67, DK4, GZMB, TOX, IFNG, MIR155HG, TNFRSF9, and RB1 (28). The third signature gene set from Long and colleagues was sorted by the log fold change between the CD19-28 CAR versus GD2-28 CAR, and the top 2,000 positive log fold change genes were selected for AUC calculation (1). Statistical significance is indicated by one or more asterisks, and the number of asterisks shown corresponds to P values less than 0.05, 0.01, 0.001, or 0.0001, respectively.

Extracellular staining for flow cytometry was performed by incubating titrated amounts of fluorescent-labeled antibodies or viability dye for 15 minutes at room temperature. Secondary staining for biotin–streptavidin conjugates was performed with a 30-minute incubation at room temperature. Acquisition was performed with a BD ARIA flow cytometer (BD Biosciences) or Attune NxT flow cytometer (Invitrogen). For flow cytometric analysis of granzyme B and perforin production, cells were stimulated with Raji-PVR in the presence of GolgiStop (BD Biosciences) for 6 hours prior to staining. Intracellular staining was performed following extracellular staining with eBioscience Foxp3/Transcription Buffer Staining Set (Invitrogen) according to the manufacturer's instructions. See Supplementary Table S4 for a complete list of flow cytometric reagents.

Flow cytometric gating was based on fluorescence minus one controls in cases where bimodal distribution was not apparent. To generate histogram comparisons of fluorescence intensity across samples run with the same cytometer settings on different days, we first performed batch correction with the function SwiftReg (70) on MATLAB_R2020b (The MathWorks, Inc.). Comparisons were then performed with concatenations of all samples containing equal proportions of the cell type of interest. For comparison of two groups without matching, a Mann–Whitney test was performed. Comparisons of two groups with matching samples were performed with Wilcoxon matched-pairs signed rank tests. For comparisons of three or more groups, a Friedman test was performed. For paired comparisons across three or more groups, a mixed-effects analysis was performed. For all plots derived from flow cytometric data, no correction was made for multiple comparisons, and all comparisons made in the statistical analysis are displayed. All statistical analyses were done using Prism 9.0.2 (GraphPad Software). Statistical significance is indicated by one or more asterisks, and the number of asterisks shown corresponds to P values less than 0.05, 0.01, 0.001, or 0.0001, respectively.

Raji cells and 293T cells were obtained from ATCC and were authenticated using short tandem repeat profiling. The cell lines were tested regularly for Mycoplasma using a Mycoplasma Detection Kit-QuickTest (Bimake). Cell lines were passaged less than 6 months and used for studies within 1 month of thawing.

293T cells (ATCC) were transfected using Lipofectamine 3000 (ThermoFisher) with a luciferase construct, human PVR lentiviral construct, or green fluorescence protein construct obtained from VectorBuilder to generate lentiviral vector and Raji cells (ATCC) were transduced. Luciferase-expressing cells were selected with puromycin for two weeks. PVR-expressing cells were selected with blasticidin (10 μg/mL) for two weeks. GFP-expressing cells were FACS sorted.

The media utilized here contained 10% fetal calf serum, 1% penicillin/streptomycin, and 0.1% ciprofloxacin in RPMI-1640 with 2.05 mmol/L L-glutamine (cRPMI). CAR-T cells were thawed and rested overnight in 5 ng/mL IL15 and 10 ng/mL IL7 in cRPMI. The next day, CAR products were either FACS sorted with anti-FMC63-FITC to obtain a pure CAR⁺ population or immediately added to culture with the Raji cell line at a 4:1 CAR-T cell to Raji cell ratio in 30 U/mL IL2. Every 3 days, CAR-T cells were counted, and the effector-to-target ratio was restored with additional Raji cells. Aliquots were taken at the indicated days for flow cytometric analysis.

Male NOD.Cg-Prkdc^scidIl2rgt^m1Wjl/SzJ (NSG) mice approximately 8 weeks old (The Jackson Laboratory) were injected i.v. with 1 million luciferase-expressing Raji-PVR cells. In the mice used to generate overall survival, 3 million CAR-T cells were injected i.v. 3 weeks following tumor inoculation. Otherwise, 5 million CAR-T cells were injected i.v. 1 week following tumor inoculation. Disease progression was monitored weekly by bioluminescence imaging using the IVIS Spectrum Imager (PerkinElmer) and analyzed with Living Image (PerkinElmer). Following CAR-T cell injection, mice received weekly 10 mg/kg intraperitoneal injections of TIGIT antibody (BMS-986207, Bristol Myers Squibb) or antibody control (Bristol Myers Squibb) for the duration of the study. Mouse kidney, spleen, and lymph nodes were isolated and homogenized for flow cytometric analysis. Blood was isolated, and red blood cells were lysed prior to flow cytometric analysis. Overall survival statistical significance was calculated with a log-rank Mantel–Cox test. Investigators were not blinded for randomization or treatment. Power analysis has been done according to publications and prior experience. Animal studies were performed under an approved institutional animal care and use committee at Case Western Reserve University.

The source R code and required Seurat objects are available at https://github.com/hwanglab/hwanglab_2021_tigitCarT or https://codeocean.com/capsule/0626385/tree/v1. The raw unfiltered FASTQ files are deposited at EGA and available at https://ega-archive.org/studies/EGAS00001005356.

P.F. Caimi reports grants, personal fees, and nonfinancial support from ADC Therapeutics, grants and personal fees from Genentech, and personal fees from GenMab, Novartis, Kite, Bristol Myers Squibb, Takeda, Lilly, and MEI Pharma outside the submitted work. M. de Lima reports other support from Miltenyi Biotec during the conduct of the study, as well as personal fees from Pfizer and Bristol Myers Squibb outside the submitted work. T.H. Hwang reports other support from KURE.AI during the conduct of the study; grants and personal fees from AITRICS outside the submitted work; and is a cofounder of a start-up company that is developing cell therapy products including CAR-T products. The company was not involved in any of the studies for this article and did not provide any support for these studies. In addition, KURE.AI has not performed any studies related to TIGIT (the focus of this article). Though there is no overlap between KURE.AI's efforts and this article, this disclosure is made, as both involve the general field of CAR-T therapy. D.N. Wald reports grants from the NIH during the conduct of the study, as well as other support from KURE.AI outside the submitted work. No disclosures were reported by the other authors.

Z. Jackson: Data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. C. Hong: Formal analysis, investigation, visualization, methodology, writing–review and editing. R. Schauner: Data curation, investigation, writing–review and editing. B. Dropulic: Resources, writing–review and editing. P.F. Caimi: Resources, writing–review and editing. M. de Lima: Resources, writing–review and editing. M.F. Giraudo: Visualization. K. Gupta: Data curation. J.S. Reese: Resources. T.H. Hwang: Conceptualization, resources, formal analysis, supervision, funding acquisition, project administration, writing–review and editing. D.N. Wald: Conceptualization, resources, formal analysis, supervision, funding acquisition, project administration, writing–review and editing.

This research was supported by the Case Comprehensive Cancer Center (P30CA043703) including the Hematopoietic Biorepository and Cellular Therapy Shared Resource and the Cytometry and Micro­scopy Shared Resource with support from the NIH Shared Instrumentation Grant (S10-NIH OD021559). This research was also supported by the NIH grant T32AI089474.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.