CAR density influences antitumoral efficacy of BCMA CAR T cells and correlates with clinical outcome

Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 1 of 15 CANCER CAR density influences antitumoral efficacy of BCMA CAR T cells and correlates with clinical outcome Paula Rodriguez-Marquez1†, Maria E. Calleja-Cervantes1,2†, Guillermo Serrano2†, Aina Oliver-Caldes3, Maria L. Palacios-Berraquero4, Angel Martin-Mallo1, Cristina Calviño4, Marta Español-Rego5, Candela Ceballos6, Teresa Lozano7, Patxi San Martin-Uriz1, Amaia Vilas-Zornoza1,8, Saray Rodriguez-Diaz1, Rebeca Martinez-Turrillas1,8, Patricia Jauregui4, Diego Alignani9, Maria C. Viguria6, Margarita Redondo6, Mariona Pascal5, Beatriz Martin-Antonio3§, Manel Juan5,10, Alvaro Urbano-Ispizua3, Paula Rodriguez-Otero4, Ana Alfonso-Pierola4,8, Bruno Paiva1,8,9, Juan J. Lasarte7, Susana Inoges4,8,11, Ascension Lopez-Diaz de Cerio4,8,11, Jesus San-Miguel1,4,8,12, Carlos Fernandez de Larrea3, Mikel Hernaez2,8,13*‡, Juan R. Rodriguez-Madoz1,8*‡, Felipe Prosper1,4,8,12*‡ Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study, we address the impact of CAR density on the functionality of BCMA CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with up-regulation of exhaustion markers and increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of gene regulatory networks using scRNA-seq identified regulons associated to activation and exhaustion up-regulated in CARHigh T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CARHigh T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with notable impact on clinical response. INTRODUCTION Chimeric antigen receptor (CAR) T cell therapies have emerged as a promising therapeutic tool against cancer, revolutionizing cancer immunotherapy (1). Second-generation CAR T cells have shown to induce impressive clinical responses in hematological malignancies, such as chemotherapy-resistant B cell leukemias and lymphomas (2–4) and multiple myeloma (MM) (5–7). Despite the high rates of remissions, not every patient achieves a complete response (CR) after CAR T cell therapy. In addition, a substantial number of patients experience a relapse of the disease. Particularly in patients with MM, despite their impressive responses, apparently, so far, there is no plateau in the survival curves after CAR T cell therapy (5–7), which contrasts with results obtained with CD19 CAR T cells in acute lymphoblastic leukemia and non-Hodgkin’s lymphomas. It is well known that CAR T cells are heterogeneous products with multiple factors contributing to their efficacy. Several studies have demonstrated how extrinsic factors, such as antigen density or tumor burden, strongly influence efficacy of CAR T cells (8,9). In addition, factors related to CAR structure, such as the costimulatory domain or the hinge length, can also affect the antitumoral potential of CAR T cells (10–12). Moreover, intrinsic cell factors, such as the differentiation state of T cells, the CD4/CD8 ratio, or the T cell polyfunctionality, have been correlated with the therapeutic efficacy and have led to the hypothesis that enrichment of CAR T cell products in T cells with a more immature phenotype may be associated with improvement in long-term responses (13–16). On the other hand, an increase in T cells with an effector or exhausted phenotype may result in a reduced persistence of CAR T cells with a decrease in clinical response (13). Previous studies identified that CAR signaling in the absence of antigen stimulation, denominated tonic signaling, is associated with early T cell exhaustion (17–20), suggesting its role in triggering premature T cell dysfunction. Additional factors related to the CAR construct may have an impact on functionality. For instance, recent studies have demonstrated that a more physiological expression of the CAR, by the integration of the transgene in the TRAC locus or the use of different promoters, can be associated with improved efficacy and reduced toxicity (21–23). These results also suggest that the density of CAR molecule in the membrane of CAR T cells might influence CAR signaling, affecting their antitumoral efficacy (23,24). However, this hypothesis and the impact on clinical efficacy have not been formally explored. 1Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. 2Computational Biology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. 3Department of Hematology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. 4Hematology and Cell Therapy Department, Clínica Universidad de Navarra (CUN), Pamplona, Spain. 5Department of Immunology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. 6Hematology Service, Hospital Universitario de Navarra, IdiSNA, Pamplona, Spain. 7Immunology and Immunotherapy Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. 8Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain. 9Flow Cytometry Core, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. 10Immunotherapy platform Hospital Sant Joan de Déu, Barcelona, Spain. 11Immunology and Immunotherapy Department, Clínica Universidad de Navarra (CUN), Pamplona, Spain. 12Cancer Center Universidad de Navarra (CCUN), Pamplona, Spain. 13Data Science and Artificial Intelligence Institute (DATAI), Universidad de Navarra, Pamplona, Spain. *Corresponding author. Email: [email protected] (M.H.); [email protected] (J.R.R.-M.); [email protected] (F.P.) †These authors contributed equally to this work. ‡These authors share senior authorship. §Present address: Department of Experimental Hematology, Instituto de Investigación Sanitaria-Fundación Jiménez Diaz, IIS-FJD, Autonomous University of Madrid, 28040 Madrid, Spain. Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 2 of 15 Technological advances in genomics, such as single-cell sequencing, have allowed a notable progress toward understanding the genomic landscape of CAR T cells, providing some mechanistic insights into proper CAR T cell function (25–29). Moreover, functional commitment of CAR T cells is governed by complex gene regulatory networks (GRNs) that control CAR T cells at baseline as well as CAR T cell dynamics after antigen recognition being essential for CAR T functionality (30). Using single-cell RNA sequencing (scRNA-seq), recent studies have identified specific T cell signatures associated with efficacy and toxicity in patients with large B cell lymphomas (26), or molecular determinants of CAR T cell persistence such as IRF7-mediated regulation of chronic interferon signaling (29), establishing this technology as a useful tool to improve efficacy of CAR T. In this study, we address the impact of CAR density on the functionality of B cell maturation antigen (BCMA) CAR T cells. Phenotypic, functional, transcriptomic, and epigenomic studies at bulk and single-cell level revealed different profiles between CAR T cells with high and low expression of the CAR molecule (CARHigh T and CARLow T cells). We show that CARHigh T cells are associated with tonic signaling and an exhausted phenotype, and identify the molecular mechanisms involved in the different functionalities of CARHigh T and CARLow T cells. We define a molecular signature associated with increased CAR density that, when applied to CAR T cell products, may predict clinical response. These results would provide a useful tool to understand the mechanisms behind proper CAR T cell function and identify biomarkers of response with potential clinical implications. RESULTS CAR T cells exhibit a wide range of CAR density on cell surface that influences CAR-mediated signaling Current CAR T cell products are generated using retroviral/lentiviral (LV) vectors that render different levels of transduction and transgene expression within the cells, consequently observing a wide range of CAR molecule densities on the surface of transduced cells (fig. S1). We hypothesized that this heterogeneity in CAR density could affect CAR-mediated signaling and, hence, influence the efficacy of CAR T cell products. To address this question, we first used a triple parameter reporter (TPR) system in Jurkat cells (31) to measure, by flow cytometry, the CAR-mediated activation of the main signaling pathways [nuclear factor of activated T cells (NFAT), nuclear factor B (NFB), and activator protein 1 (AP1)] after tumor recognition using CAR T cells with different levels of CAR on the cell surface. Jurkat-TPR cells were infected with a secondgeneration CAR construct targeting BCMA, with 4-1BB as costimulatory domain, that was further modified to include a truncated epidermal growth factor receptor (EGFRt) reporter, facilitating the measurement of CAR level (fig. S2A). Then, subsets of Jurkat-TPR cells presenting different levels of CAR (termed CARHigh and CARLow cells) were selected according to the fluorescence intensity (FI) of EGFRt (top and bottom FI quartiles, respectively; see Materials and Methods) (fig. S2B) and analyzed after coculture with different BCMA-expressing MM cell lines. We observed significantly increased levels of activation in the three mentioned pathways within the CARHigh population (fig. S2C). Moreover, a significant increase of activation of CARHigh cells was also observed even in the absence of tumor cells, indicating an increase in tonic signaling at baseline (fig. S2D). We consistently observed this functional pattern with other CAR constructs targeting CD19, CD33, and HER2 (fig. S2, C and D), indicating that a higher density of CAR molecules in the cell surface increases both the tonic signaling and the signal transduction after tumor encountering. CAR density influences antitumoral response of CAR T cells targeting BCMA To further analyze the effect of CAR density on antitumoral efficacy, we characterized CAR T cells from 10 healthy donors that were generated using a BCMA-targeting CAR construct (derived from ARI-0002h) coexpressing blue fluorescent protein (BFP) as a reporter marker (fig. S3). CAR T cells were sorted into CARHigh and CARLow subpopulations based on the expression of BFP (Fig.1A and fig. S4A). CARHigh T cells include those cells with a BFP FI>1.2 × 104 (average FI, 26,861±5795), while the CARLow T cell subpopulation was restricted to BFP FI<4 × 103 (average FI, 2513±388). These FI values corresponded to the top and bottom FI quartiles, respectively (fig. S4A). Then, BFP FI values were used to quantify the number of CAR molecules on the surface of these two CAR T cell subpopulations using an antibody-binding capacity bead assay. CARHigh T cells presented more than 5000 CAR molecules per cell, while the number of molecules per cell in CARLow T cells was below 1500 (fig. S4B). Because integration site could affect CAR expression, we performed an integration site analysis in CARHigh T and CARLow T cells. No differences were observed in the integration profile between cells with different CAR densities, being most of the integrations located at active sites (i.e., promoter and intronic regions) as expected for LV vectors (fig. S4, C and D) (32,33). However, vector copy number (VCN) analysis revealed a significantly higher number of viral integrations within CARHigh T cells, with an average of 4.5±1.3 integrations in CARHigh T cells versus 1.8±0.3in CARLow T cells (fig. S4E). This increased VCN resulted in a significant increase in CAR mRNA expression levels (fig. S4F), which could explain the increased CAR density observed in these cells rather than due to the specific integration sites. We found increased cytotoxic activity and greater levels of interferon- (IFN-), interleukin-2 (IL-2), tumor necrosis factor– (TNF), and granzyme B (GZMB) production in CARHigh T cells (Fig.1,BandC, and fig. S4G). To determine the translational value of these findings, we examined CAR levels in CAR T cell products from an academic clinical trial (CARTBCMAHCB-01; NCT04309981) (fig. S4H). We observed an increased invitro lytic activity in those CAR T cell products enriched in CARHigh T cells (>30% of cells with >5000 CAR molecules per cell) (fig. S4I). Next, we analyzed the phenotype of CARHigh T and CARLow T cells before and after stimulation with tumor cells. No differences were observed in the CD4/CD8 ratio at any condition (fig. S5A). However, at baseline, we observed a statistically significant enrichment of central memory (TCM) and effector memory (TEM) phenotypes within CARHigh T cells, with concomitant reduction of naïve (TN) and stem central memory (TSCM) cells in both CD4+ and CD8+ subsets (Fig.1D and fig. S5B). After antigen stimulation, both populations acquired a TEM-TE phenotype, although increased numbers of TE were observed in CARHigh T cells (Fig.1D and fig. S5C). These results suggest a higher degree of differentiation in CARHigh T cells even in the absence of antigen stimulation. Moreover, we observed that CARHigh T cells presented an increase in basal activation, with significant higher levels of human leukocyte antigen (HLA)–DR+ and Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 3 of 15 CD137+ cells, along with a higher percentage of CD8+ T cells expressing a combination of two or more markers of exhaustion (LAG3, TIM3, and/or PD1) (Fig.1,EtoG). These differences in cell exhaustion increased after antigen stimulation, with more than 30% of CD8+ CARHigh T cells expressing an exhausted phenotype (Fig.1G). A continuous repeated invitro stimulation for 21 days with tumoral cells revealed increased differences in the differentiation and exhausted phenotype, and an impaired proliferation potential of CARHigh T cells (Fig.1H and fig. S5, D and E). Moreover, because Jurkat reporter system revealed signs of tonic signaling in CARHigh cells, we further analyze this phenomenon in CAR T cells using an NFAT–green fluorescent protein (GFP) reporter vector. In the absence of tumoral cells, we observed an increased number of cells expressing significantly higher levels (FI) of GFP within CARHigh T cells, corroborating the tonic signaling in this population (fig. S5F). 0510 15 20 25 0 1 × 10 6 2 × 10 6 3 × 10 6 4 × 10 6 5 × 10 6 Proliferation Days Cell number CAR High T A R L o w T AB D C 0 50 100 % CD4 + cells Basal Stim CAR High T C A R L o w T CAR High T C A R L o w T 0 50 100 % CD8 + cells BasalStim CAR High T C A R L o w T CAR High T C A R L o w T T N T SCM T CM T EM T E Basal Stim 0 20 40 60 80 100 % HLA-DR + cells ns CD4 + Basal Stim 0 20 40 60 80 100 ns CD8 + CAR High T C A R L o w T Basal Stim 0 5 10 15 20 25 % Exhausted cells ns ns CD4 + BasalStim 0 20 40 60 CD8 + BCMA CAR T cells BCMA-expressing tumoral cells (ARP1-GFPLuc) Tcell LV Sorter C A R L o w T Basal CARHigh T Stimulate d A R L o w T CARHigh T 0 1000 2000 3000 4000 pg/ml CAR High T C A R L o w o T 0 50 100 % Lysis Ratio 5:1 0 50 100 Ratio 1.6:1 IFN-γ CAR High T A R L o w o o T CAR High T C A R L o w o o T BasalStim 0 5 10 15 20 25 % CD137 + cells CD4 + Basal Stim 0 10 20 30 40 CD8 + CAR High T C A R L o w T F E GH Fig. 1. CARHigh T cells present increased in vitro antitumoral efficacy and exhausted phenotype. An in vitro functional and phenotypic characterization was performed on CAR T cells targeting BCMA presenting different densities of the CAR molecule. (A) Schematic representation of the procedure. CAR T cells were sorted into CARHigh T and CARLow T cell subpopulations via BFP expression. Analyses were performed at basal state or after stimulation with tumor cells expressing BCMA. (B) Quantification of the cytotoxic activity of CARHigh T and CARLow T cells against ARP1-GFPLuc at different effector to target (E:T) cell ratios. The percentage of specific lysis (average of three technical replicates) for each CAR T cell production (n = 6) is depicted. (C) Quantification of IFN- levels in supernatants from cytotoxic assays (ratio of 5:1) measured by enzyme-linked immunosorbent assay (ELISA). The cytokine concentration (pg/ml; average of three technical replicates) for each CAR T cell production (n = 6) is depicted. (D) Analysis of the phenotype of CARHigh T and CARLow T cell populations before (Basal; n = 10) and after stimulation (Stim; n = 5) with ARP1-GFPLuc tumor cells. Mean ± SEM of each T cell subpopulation within CARHigh T and CARLow T cells is depicted. TN, naïve; TSCM, stem central memory; TCM, central memory; TEM, effector memory; TE, effector. Analysis of the expression of HLA-DR (E), CD137 (F), and a combination of >2 exhaustion markers (LAG3, TIM3, and/or PD1) (G) in CARHigh T and CARLow T cells before (Basal; n = 10) and after stimulation (Stim; n = 5) with ARP1-GFPLuc tumor cells. ns, not significant. (H) Proliferation of CARHigh T and CARLow T cells after continuous repeated in vitro stimulation for 21 days with tumoral cells. Wilcoxon test for paired samples (B and C). Two-way analysis of variance (ANOVA) with Sidak’s multiple comparison (E to G). *P < 0.05, **P < 0.01, and ***P < 0.001. Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 4 of 15 To determine whether different CAR densities would affect longterm antitumor potential, we further evaluated CAR T cell efficacy invivo using a stress test in NOD-SCID-Il2rg−/− (NSG) mice (21,34). Thus, MM1.S cells expressing luciferase (3 × 106 cells peranimal) were transplanted in NSG mice, and after 14 days, different doses of fluorescence-activated cell sorting (FACS)–sorted CARHigh T or CARLow T cells (5 × 105, 1.5 × 105, and 0.5 × 105) were infused into the animals (Fig.2A). All the animals treated with CAR T cells presented increased survival compared to controls with a dose-dependent response. At the lower dose, we observed an increased presence of tumoral cells in the animals treated with CARHigh T cells, as demonstrated by luciferase measurements (Fig.2,BandC, and fig. S6A), which lead to a tendency of reducing the survival of the animals, although no statistical differences were observed (Fig.2D). Increased tumor progression in animals treated with CARHigh T cells was confirmed by measuring the presence of tumoral cells in the bone marrow (BM) 21 days after treatment (Fig.2E). We further characterized CAR T cell persistence, phenotype, and functionality at different time points after cell administration. First, we analyzed the infiltration of CAR T cells in the BM of the animals at 7 and 21 days after CAR T administration, and we observed a statistically significant reduced number of infiltrated CAR T cells in those animals treated with CARHigh T cells (Fig.2F). Moreover, CARHigh T cells also presented a more differentiated phenotype at day 7 with increased percentage of terminally effector cells in both CD4+ and CD8+ subpopulations (Fig.2G and fig. S6B). Last, functional exvivo analysis of isolated CARHigh T and CARLow T cells 7 days after administration showed that CARHigh T cells were more cytotoxic with increased production of not only cytokines related to effector function (i.e., PRF1, GZMA, and GZMB) but also exhaustion markers (PD1, LAG3, and TIM3) (fig. S6, C and D). Overall, these results indicate that CARHigh T cells show increased activation and tonic signaling associated with increased invitro cytotoxicity. Moreover, the more differentiated and exhausted phenotype observed in CARHigh T cells (both invitro and invivo) together with the decreased BM infiltration capacity resulted in lower invivo B A 020 40 50 60 70 0 50 100 Day % Survival 020 40 50 60 70 0 50 100 Day 020 40 50 60 70 0 50 100 Day Control 0.5 × 105 cells1.5 × 105 cells5 × 105 cells CA RT High CA RT Low 0 110 4 2 10 4 3 10 4 4 10 4 Luciferase at day 44 ph/s/cm 2 /sr CAR High T CAR Low T 5 × 10 5 1.5 × 10 5 0.5 × 10 5 CD 100 1000 10,000 CAR T/10 6 lymphocytes CAR High T CAR Low T Day 7 0.0 0.5 1.0 10 20 30 Tumoral cells Day 7 Day 21 % MM1.S cells CAR High T CAR Low T CAR High T CAR Low T Control Control 0 20 40 60 % Cells CAR High T CAR Low T CAR High T CAR Low T CD4CD8 T E day 7 0 20 40 60 CD4 CD8 ns ns CAR High T CAR Low T CAR High T CAR Low T T E day 21 10 100 1000 CAR High T CAR Low T Day 21 EF G Fig. 2. In vivo antitumoral efficacy of CAR T cells with different CAR densities. (A) Schematic representation of the experimental procedure. NGS mice were injected intravenously on day 0 with 3 × 106 MM1.S-GFPLuc cells per animal. After 14 days, 5 × 105, 1.5 × 105, or 0.5 × 105 of CARHigh T or CARLow T cells were injected intravenously. Bioluminiscence analysis (BLI) was performed on days 28 and 44. Animal survival was monitored until the end of the experiment (day 67). On days 21 (7 days after CAR T administration) and 35 (21 days after CAR T administration), a subset of animals was sacrificed to analyze the presence of tumor and CAR T cells. (B) BLI images at the indicated days of control mice (n = 5) or treated with CARHigh T cells (n = 4 to 5) or CARLow T cells (n = 4 to 5). (C) Quantification of BLI (photons/s/cm2/sr) in the different group of animals as a measurement of tumor growth. (D) Survival of control mice (n = 5) or treated with CARHigh T cells (n = 4 to 5) or CARLow T cells (n = 4 to 5) at the indicated doses. (E) Quantification of the tumor cells present in the BM of control mice (n = 4) or treated with CARHigh T cells (n = 5) or CARLow T cells (n = 5) at days 7 and 21 after CAR T cell administration. (F) Quantification of the CAR T cells present in the BM of animals treated with CARHigh T cells (n = 6) or CARLow T cells (n = 6) at days 7 and 21 after CAR T cell administration. (G) Analysis of the phenotype of CARHigh T and CARLow T cell populations at days 7 and 21 after CAR T cells administration. Mean ± SEM of TE cell subpopulation within CD4+ and CD8+ subset of CARHigh T and CARLow T cells is depicted. Mantel-Cox (log-rank) test (D), Kruskal-Wallis test (E), Mann-Whitney test (F), and two-way ANOVA with Sidak’s multiple comparison (G). *P < 0.05 and **P < 0.01. Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 5 of 15 antitumoral effect under stressed conditions, which could have an impact on the clinical response. CARHigh T cells display different transcriptomic and chromatin landscape with increased tonic signaling and T cell activation Given the differences observed in phenotype and persistence between CARHigh T and CARLow T cells, we delved into the transcriptomic and epigenetic landscape of the two subpopulations of CAR T cells. Sorted CD4+ and CD8+ CARHigh T and CARLow T cell populations from six different CAR T cell productions were profiled using high-throughput RNA-seq and assay for transposase-accessible chromatin with sequencing (ATAC-seq). Transcriptomic analysis revealed less than 100 differentially expressed genes [DEGs; false discovery rate (FDR)<0.05, log2 fold change (Log2FC) > 2] between CARHigh T and CARLow T cells (table S1). Similarly, the analysis of the ATAC-seq data revealed (i) a similar peak distribution in both populations, with >60% of the peaks located within the promoter regions and the first intron, and (ii) a limited number of differential accessible regions identified between CARHigh T and CARLow T cells (fig. S7 and table S2). These small transcriptomic and epigenomic differences were enough to separate CARHigh T cells from CARLow T cells in a principal components analysis in both CD4+ and CD8+ T cell subsets (Fig.3A and fig. S8A). DEGs between CARHigh T and CTLA-4 PC1: 47% variance PC2: 30% variance PC1: 51% variance PC2: 14% variance R N A - s e q A T A A A T T C - s e q C A R H i g h T C A R L o w T CAB D CAR High T CAR Low T 0 1 × 10 3 2 × 10 3 TNFRSF4 CAR High T CAR Low T 0 5 × 10 2 1 × 10 3 TNFRSF9 0 3 × 10 3 6 × 10 3 HLA-DRA CAR High T CAR Low T 0 3 × 10 4 6 × 10 4 CD74 E x p r e s s i o n E x p r e s s i o n 0.0 6.0 × 10 2 1.2 × 10 3 CIITA 0 1 × 10 2 2 × 10 2 Fig. 3. Transcriptomic profile and chromatin landscape of CD8+ CARHigh T cells. The transcriptomic and epigenetic landscape of sorted CD8+ and CD4+ (see fig. S8) CARHigh T and CARLow T cells (n = 6) was profiled using high-throughput RNA-seq and ATAC-seq. (A) RNA-seq and ATAC-seq principal components (PC) analysis, corrected by patient heterogeneity, of sorted CD8+ CAR T cell subsets. Percentage of variance explained by PC1 and PC2 is depicted. (B) Heatmap of DEGs between CD8+ CARHigh T and CARLow T cells associated to genes involved in tonic signaling and T cell activation. (C) Quantification of CTLA-4, CIITA, HLA-DRA, CD74, TNFRSF4 (OX40), and TNFRSF9 (4-1BB) gene expression in CD8+ CARHigh T and CARLow T cells. (D) UCSC genome browser tracks of CTLA-4, CIITA, HLA-DRA, TNFRSF9, CD74, and TNFRSF4 showing differential peaks from ATAC-seq analysis between CD8+ CARHigh T and CARLow T cells. Wilcoxon test for paired samples (C). *P < 0.05. Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 6 of 15 CARLow T cells were associated with genes involved in tonic signaling and T cell activation (Fig.3B and fig. S8B). In particular, CARHigh T cells showed increased expression of genes related to lymphocyte activation, such as HLA-DRA, CIITA, and CD74, as well as costimulatory molecules, such as CTLA-4, TNFRSF4 (OX40), and TNFRSF9 (4-1BB) (Fig.3C and fig. S8C). These results corroborated our phenotypic observations (see previous sections). While reduced overlapping was observed between DEGs and differential peaks, those genes showing differential expression and chromatin accessibility were mainly related to T cell activation and costimulation (Fig.3D and fig. S8, D and E). Together, these results suggest that small differences in gene expression and chromatin accessibility associated to increased CAR levels can substantially modify the overall phenotypic and functional profile of CAR T cells. Single-cell sequencing reveals specific distribution of CARHigh T cells To better understand the heterogeneity of CAR T cells and the influence of CAR level on their transcriptomic profile, we performed single-cell transcriptomic analysis on 43,981 CAR T cells from three independent productions. After quality control and filtering, we performed an integrated analysis and identified 23 clusters of CAR T cell subpopulations (fig. S9A). Clusters identified based on cell cycle gene signatures, those containing high levels of mitochondrial genes, and clusters lacking expression of T cell markers were excluded from further analysis (fig. S9, B to F). Cell types and functional states of the remaining 15 clusters (containing 28,117 cells with range of 7416 to 105,093 cells per donor) were defined according to the expression of previously described canonical markers (Fig.4,AandB) (26–28). Within CD4+ cells, we identified early memory (IL7R), memory (TCF7, CCR7, and CD27), activated (HLA-DRA and OX40), cytotoxic (GZMA and PRF1), and T helper 2 (TH2) (GATA3) CAR T cells, with a minority of other subtypes including cells expressing genes related to glycolysis and IFN response. Among CD8+ cells, we distinguished memory (TCF7 and CCR7) and cytotoxic (GZMA, PRF1, and NKG7) CAR T cells. Furthermore, in accordance with the phenotypical results, we identified a pre-exhausted cluster of CD8+ CAR T cells characterized by 0134568910 12 15 16 17 19 21 Cluster C0.CD4 early memory C1.CD4 TH2 helper C3.CD8 memory C4.CD4 memory C5.CD4 early memory C6.CD4 activated C8.CD8 cytotoxic C9.CD8 pre-exhausted C10.CD4 cytotoxic C12.CD4 TH2 helper C15.CD4 glycolysis C16.CD4 IFN response C17.CD4 activated C19.CD4 Treg 21.CD4 cytotoxic CD4 TCF7 HLA−DRA GZMA LAG3 CD8A CCR7GATA3 PRF1 TIGIT IL7R NOSIP CD69 IL2RA GATA3 TCF7 CCR7 CD27 TCF7 CCR7 CD27 IL7R NOSIP HLA-DRA HLA-DPA1 HLA-DRB1 HLA-DQA1 HLA-DPB1 HLA-DRB5 HLA-DMA GZMA GZMB GZMK GZMH PRF1 CD7 NKG7 GZMA GZMB NKG7 TIGIT LAG3 PRF1 CTSC CST7 PGK1 GAPDH LDHA ALDOC ENO1 OAS1 MX1 OASL MX2 ISG15 ISG20 OX40 41BB GITR CD44 CD70 IRF7 GNLY GZMB CD69 IL2RA GATA3 CD4 early memory CD4 TH2 helper CD4 early memory CD8 memory CD4 memory CD4 activated CD8 cytotoxic CD8 pre-exhausted CD4 cytotoxic CD4 TH2 helper CD4 glycolysis CD4 IFN response CD4 activated CD4 Treg CD4 cytotoxic C AB C Fig. 4. Characterization of CAR T cells at single-cell level. CAR+ (BFP+) cells from three independent CAR T cell productions were assayed by scRNA-seq. (A) An overview of the 28,117 cells that passed quality control and filtering for subsequent analyses in this study. Uniform Manifold Approximation and Projection (UMAP) plot showing the 15 clusters that were analyzed. (B) Heatmap showing signature genes of each cluster and putative assignments to cell types according to canonical marker genes. (C) UMAP plot overlaid with mRNA expression of T cell markers (CD4 and CD8A), memory markers (TCF7 and CCR7), activation markers (HLA-DRA and GATA3), effector enzymes (GZMA and PRF1), and exhaustion markers (LAG3 and TIGIT). Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 7 of 15 the expression of cytotoxic genes along with inhibitory receptors, such as LAG3 and TIGIT (Fig.4,BandC) (35). Analysis of V(D)J rearrangement showed a polyclonal diversity of CAR T cells within all the clusters, indicating no specific enrichment of any particular clone (table S3). To robustly identify CAR T cells with high expression of the CAR construct in our single-cell data, we developed gene signatures associated to both CD4+ and CD8+ CARHigh T cells using all DEGs (Log2FC>1, FDR<0.05) from bulk RNA-seq analysis between CARHigh T and CARLow T cells (table S4 and fig. S10A), as these populations were sorted on the basis of CAR protein expression (see Materials and Methods). We found a better correlation between the gene signature and CAR protein level than the one observed between the expression of the CAR gene and its protein expression (fig. S10B). Moreover, the possibility to extrapolate these gene signatures to other datasets was assessed via leave-one-out crossvalidation (fig. S10C). Then, we annotated CARHigh T cells in our single-cell data using the developed signatures (Fig.5A), and we observed that CARHigh T cells mainly localized within activated CD4+ cells, representing more than 50% of the cells in cluster 6 and almost 80% of the cells in cluster 17 (Fig.5B). Furthermore, in the CD8+ T cell compartment, CARHigh T cells were more represented in cluster 9, showing a pre-exhausted phenotype (Fig.5B). Moreover, CARHigh T cells were significantly enriched in activation and tonic signaling signatures (Fig.5,CandD), results that are in accordance with our previous phenotypic and transcriptomic analysis performed in FACS-sorted CARHigh T and CARLow T cell populations. In addition, we perform this analysis using an independent single-cell public dataset of CAR T products targeting CD19 from patients with diffuse large B cell lymphoma (DLBCL) (26), obtaining similar results (fig. S11). Together, our results would indicate a strong association among tonic signaling, CAR T cell activation, and cell exhaustion that is increased in CAR T cells with high CAR density defined by either the gene signature or the protein expression. CAR density is associated with differential activation of regulatory networks To elucidate the molecular regulation of CARHigh T cells, we applied SimiC (36), a novel GRN inference algorithm for scRNA-seq data that imposes a similarity constraint when jointly inferring the GRNs for each specific cell state. On the basis of this analysis, we observed regulons [a transcription factor (TF) and its associated target genes] that were similarly activated between CARHigh T and the rest of the CAR T cells (fig. S12), such as regulons implicated in T cell differentiation (GATA3 and RUNX3) and signal transduction (STAT1, REL, RELA, and JUN/AP1) (37–39). On the other hand, we identified some regulons that were more active in CARHigh T cells, such as STAT3, a TF associated with development and maintenance of T cell memory (40), or ARID5A, a TF related to the control of the stability of STAT3 (41) (fig. S12), and other regulons that presented reduced activity in CARHigh T cells, such as BTG2, a TF related to the prevention of proliferation exacerbation and spontaneous activation (42) (fig. S12). These changes in regulon activity could explain the increased central memory phenotype and also provide a regulatory mechanism for the increased activation observed in CARHigh T cells. We also observed regulons presenting a multimodal activation profile. To determine whether this distribution might be related to different activity between clusters, we computed the distribution of the regulon activity in each cluster (provided it contains at least 5% of CARHigh T cells) (fig. S12). As an example, we observed that the activity of RFX5 regulon, a member of the RFX family that interacts with HLA class II genes and promotes their transcription (43,44), was overexpressed in CD8+ CARHigh T cells independently of the T cell subtype. However, RFX5 regulon activity progressively increased through CD8+ differentiation when we analyzed CD8+ CAR T cells that were not CARHigh T cells from memory to cytotoxic and, finally, to pre-exhausted cells (Fig.6). In addition, we searched for regulons that could explain the exhausted phenotype observed in CARHigh T cells. We observed that NR4A1 and MAF regulons, already described as drivers of T cell exhaustion (45–47), were more active in CARHigh T cells (Fig.6). On the other hand, the SATB1 regulon, related to PD1 inhibition (48), presented lower activity in CARHigh T cells (Fig.6). All these results may shed light on the molecular mechanism underpinning the exhausted phenotype observed in CARHigh T cells. Moreover, we integrated the GRN analysis with our ATAC-seq data form sorted CARHigh T and CARLow T cells. We observed that the binding motifs of the TF from regulons presenting increased activity in CARHigh T cells, such as ARID5A, RFX5, or MAF, were enriched in regions with differential accessibility between CARHigh T and CARLow T cells (fig. S13 and table S8). These results would suggest a functional association between the epigenetic regulation and the regulons differentially active in CARHigh T cells. Overall, our GRN analysis using SimiC provides mechanistic insights into the regulatory networks behind the phenotypic and functional differences observed in CARHigh T cells, identifying regulons that regulate T cell function previously described, supporting the usefulness of this methodology. The use of SimiC permits the generation of a hypothesis based on identified regulatory factors that could be modulated, ultimately to the design of optimized CAR T therapies. CARHigh T gene signature is associated with clinical response Given the differences in functionality between CARHigh T and CARLow T cells, we reasoned that CAR density might have an impact on the clinical response to CAR T cell therapies. To evaluate this hypothesis, we applied the gene signatures associated with CARHigh T cells to infusion products from several clinical trials with public transcriptomic data available (25,26). We first applied our CD4+ and CD8+ signatures to bulk RNA-seq data of 34 infusion products from adult chronic lymphocytic leukemia (CLL) patients treated with CTL019 (25). We observed that products from patients with poor clinical response (partial responders and nonresponders) presented a significant higher score of both CD4+ and CD8+ CARHigh T signatures (Fig.7A). We also assessed CARHigh T cell signature on an scRNA-seq dataset comprising anti-CD19 CAR T infusion products from 24 patients with DLBCL (26). We found that the products from nonresponder patients were significantly enriched in CD8+ CARHigh T cells (Fig.7B), supporting the correlation between CAR density and clinical response. Last, we examined the correlation between the clinical response and the expression of CAR measured by FACS in the cell products of an academic clinical trial assessing ARI-0002h, a CAR T cell targeting BCMA (CARTBCMA-HCB-01; NCT04309981). Patients with partial response (PR) [less or equal than very good PR (VGPR)] showed an increase in the number of CARHigh T within the infusion product versus the patients presenting stringent CR (sCR) (Fig.7C). Moreover, a shorter duration of response was observed in patients with increased percentage of CARHigh T cells (P=0.04, as continuous variable in Cox regression Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 8 of 15 model). No statistical correlation was observed with the development or grade of cytokine release syndrome (CRS), although the patients of this cohort presented only low-grade CRS (grades 1 and 2). Overall, our data suggest that CAR T therapeutic products enriched in T cells with high CAR density on the membrane would negatively affect the clinical response. DISCUSSION The functionality of CAR T cells relies on the interaction between the tumor and engineered T cells (1). However, as living drugs, CAR T cells are heterogeneous products in which intrinsic and extrinsic factors can influence their functionality having a significant impact on their clinical efficacy (8–16). Among others, the level of UMAP 1 UMAP 2 CD4 CD8 CARHigh T distribution 0 20 40 60 80 0134568910121516171921 Cluster % of cells UMAP 1 UMAP 2 Genes activation UMAP 1 UMAP 2 Genes tonic −0.5 0.0 0.5 1.0 Signature score C6.CD4 activated *** 0 20 40 Signature score Activation genes −10 0 10 20 30 Signature score Tonic signal C8.CD8 cytotoxic 0 20 40 Signature score 0 10 20 30 Signature score C9.CD8 pre-exhausted 0 20 40 60 80 Signature score 0 10 20 30 40 50 Signature score *** *** *** *** AB CD CARHigh T Non-CARHigh T CARHigh T Non-CARHigh T CARHigh T Non-CARHigh T Fig. 5. Single-cell sequencing reveals specific distribution of CARHigh T cells. Annotation and further analysis of CARHigh T in the single-cell data were performed by applying the gene signatures, developed in this work, associated to both CD4+ and CD8+ CARHigh T cells that showed higher correlation with the CAR protein level than that yielded by the CAR gene expression. (A) UMAP plot showing CARHigh T cell distribution across analyzed cells. (B) Quantification of the percentage of CARHigh T cell along the different clusters. In accordance with phenotypic results, CARHigh T cells are mainly localized within activated CD4+ cells (clusters 6 and 17) and pre-exhausted CD8+ T cells (cluster 9). (C) UMAP plots overlaid with the score of activation and tonic signaling signatures, showing their distribution across cells. CARHigh T cells were enriched in the score for both signatures. (D) Quantification of the signature score in CARHigh T cells from clusters 6 (CD4+ activated), 8 (CD8+ cytotoxic), and 9 (CD8+ pre-exhausted), in comparison with the rest of the cells, for both activation and tonic signaling signatures. CARHigh T cells presented a significant increase for both signatures in almost all three clusters analyzed. Wilcoxon test (D). ***P < 0.001. Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025 Rodriguez-Marquez et al., Sci. Adv. 8, eabo0514 (2022) 30 September 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 9 of 15 antigen expression has been associated with antitumor response (8,24), with antigen loss representing one of the main mechanisms of resistance to CAR T therapies (49). Our study contributes to identify new determinants of CAR T cell function, demonstrating a clear role of the level of CAR expression on the functionality of CAR T cells. Our results indicate that high levels of CAR expression are associated with increased tonic signaling and a cell exhausted phenotype, characterized by the expression of multiple inhibitory receptors, such as PD1, CTLA4, LAG3, TIM3, and TIGIT, among others. This phenotype has been associated to reduced responses and worse long-term relapse-free survival (13,25,50), which is consistent with our findings demonstrating decreased responses in patients with increased levels of CARHigh T cells in different hematological malignancies. Previous studies have demonstrated that constitutive signaling induced by multiple factors related to the different CAR moieties (51,52) is associated to early T cell exhaustion (17–20). For instance, a recent study has shown fundamental differences in CAR signaling between CAR T cells with CD28 or CD8 transmembrane domains (TMDs) related to the heterodimerization potential of the different TMDs (53). Our results indicated that an increased density of the CAR molecule in the surface of the T cells (CARHigh T cells), produced by a higher number of viral integrations and subsequently increased expression, could be enough to trigger tonic signaling. Although the molecular mechanism should be further explored, the spontaneous clustering of the chimeric receptor molecules, leading to the CAR activation in the absence of antigen, could be a reasonable explanation. However, other mechanisms cannot be discarded NR4A1 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Density 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 RFX5 C3. CD8 memory C8. CD8 cytotoxic C9. CD8 pre-exhausted CAR High T Non-CARHigh T MAF 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Density SATB1 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Density NR4A1 RFX5 Density MAF SATB1 AB Fig. 6. CAR density is associated with differential activation of regulatory networks. Dynamics in GRNs of CARHigh T cells were analyzed by SimiC, a novel GRN inference algorithm for scRNA-seq data that imposes a similarity constraint when jointly inferring the GRNs for each specific cell state. Histograms showing the activity score during CD8+ T cell differentiation in CARHigh T cells versus the rest of the cells (A) and the inferred gene network (B) of regulons RFX5, NR4A1, MAF, and SATB1. Regulon activity of RFX5, a member of the RFX family that interacts with HLA class II genes and promotes their transcription, and NR4A1 and MAF, which have been described as drivers of T cell exhaustion, was already high in CARHigh T cells independently of the phenotype; meanwhile, in the rest of the cells, their activity progressively increased from CD8+ memory (cluster 3) to CD8+ cytotoxic (cluster 8) and finally to CD8+ pre-exhausted (cluster 9) phenotypes. In contrast, the activity of SATB1 regulon, related to PD1 inhibition, was reduced in CARHigh T cells. Downloaded from https://www.science.org at Instituto de Salud Carlos III / Majadahonda Center on November 28, 2025