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The BTLA–HVEM axis restricts CAR T cell efficacy in cancer

Nature Immunology volume 25, pages 1020–1032 (2024)Cite this article

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Abstract

The efficacy of T cell-based immunotherapies is limited by immunosuppressive pressures in the tumor microenvironment. Here we show a predominant role for the interaction between BTLA on effector T cells and HVEM (TNFRSF14) on immunosuppressive tumor microenvironment cells, namely regulatory T cells. High BTLA expression in chimeric antigen receptor (CAR) T cells correlated with poor clinical response to treatment. Therefore, we deleted BTLA in CAR T cells and show improved tumor control and persistence in models of lymphoma and solid malignancies. Mechanistically, BTLA inhibits CAR T cells via recruitment of tyrosine phosphatases SHP-1 and SHP-2, upon trans engagement with HVEM. BTLA knockout thus promotes CAR signaling and subsequently enhances effector function. Overall, these data indicate that the BTLA–HVEM axis is a crucial immune checkpoint in CAR T cell immunotherapy and warrants the use of strategies to overcome this barrier.

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Fig. 1: The BTLA–HVEM axis is enriched in HL.
Fig. 2: BTLA knockout in CD30-directed CAR T cells augments anti-HL function.
Fig. 3: BTLA knockout enhances CART19 anti-tumor function in DLBCL.
Fig. 4: BTLA restricts CAR T cells in the functional TME.
Fig. 5: BTLA knockout drives CART19 effector programming in vivo.
Fig. 6: BTLA inhibits CAR T cells via ITIMs Y257 and Y282.
Fig. 7: Expression of BTLA in patient CART19 cells correlates with clinical response.

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Data availability

scRNA-seq for HL biopsies have been deposited in the Gene Expression Omnibus under the accession code GSE232761. scRNA-seq for circulating and tumor-infiltrating CAR T cells have been deposited in the Gene Expression Omnibus under the accession code GSE263282. Some patient-related data are not included in the paper as they were generated from clinical trials and may be subject to patient confidentiality. All other data are present in the article and supplementary files or from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Code used for scRNA-seq analyses is available from the corresponding authors upon reasonable request.

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Acknowledgements

We acknowledge the group of C. Steidl at the University of British Columbia, including L. Chong, who kindly shared with us their raw scRNA-seq data of biopsies from 22 patients with HL and 5 individuals with RLN. We also acknowledge the Human Immunology Core and the Stem Cell Xenograft Core, including J. Glover and T. Secreto. We also thank the Penn Next Generation Sequencing Core, namely J. Schug, O. Smirnova and K. Smith for their generous for assistance with all sequencing projects. We also thank the Penn Institute for Immunology CyTOF Core, namely T. Ohtani. In addition, we thank I. Maillard and his lab (Hospital of the University of Pennsylvania) for generously gifting us CD45.1+ Balb/c mice. Furthermore, several of the results published here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). This research was supported by the NSF Graduate Research Fellowship Program (NSF-GRFP), DGE 1845298 (P.G.); NIH R00CA212302 and R01-37- CA262362-03 (M.R.), the Laffey McHugh Foundation (M.R.); and Berman and Maguire Funds for Lymphoma Research at Penn (M.R.). This research was also funded in part by NIH grant numbers AI130197 (D.L.) and CA267368 (D.L.). Elements of Figs. 1, 4, 6 and 7 and Extended Data Figs. 1, 3, 4, 6, 8 and 9 were created with BioRender.com.

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Authors and Affiliations

  1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Puneeth Guruprasad & Marco Ruella

  2. Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Puneeth Guruprasad, Alberto Carturan, Yunlin Zhang, Ruchi P. Patel, Akshita Joshi, Ivan Cohen, Maksim Shestov, Guido Ghilardi, Jaryse Harris, Raymone Pajarillo, Mathew Angelos, Yong Gu Lee, Jesse Rodriguez, Michael Wang, Aasha Gupta, Ositadimma H. Ugwuanyi, Seok Jae Albert Hong, Audrey C. Bochi-Layec, Christopher T. Sauter, Linhui Chen, Luca Paruzzo, Olga Shestova, Patrizia Porazzi & Marco Ruella

  3. Department of Medicine, Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Puneeth Guruprasad, Alberto Carturan, Yunlin Zhang, Ruchi P. Patel, Ivan Cohen, Maksim Shestov, Guido Ghilardi, Jaryse Harris, Raymone Pajarillo, Mathew Angelos, Yong Gu Lee, Michael Wang, Hatcher J. Ballard, Ositadimma H. Ugwuanyi, Seok Jae Albert Hong, Audrey C. Bochi-Layec, Christopher T. Sauter, Linhui Chen, Luca Paruzzo, Olga Shestova, Stephen J. Schuster, Jakub Svoboda, Patrizia Porazzi & Marco Ruella

  4. Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA

    Puneeth Guruprasad, Alberto Carturan, Yunlin Zhang, Ruchi P. Patel, Akshita Joshi, Ivan Cohen, Maksim Shestov, Guido Ghilardi, Jaryse Harris, Raymone Pajarillo, Mathew Angelos, Yong Gu Lee, Jesse Rodriguez, Michael Wang, Hatcher J. Ballard, Aasha Gupta, Ositadimma H. Ugwuanyi, Seok Jae Albert Hong, Audrey C. Bochi-Layec, Christopher T. Sauter, Linhui Chen, Luca Paruzzo, Olga Shestova, Stephen J. Schuster, Jakub Svoboda, Patrizia Porazzi & Marco Ruella

  5. Department of Pathology, Immunology and Laboratory Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA

    Jong Hyun Cho & Dongfang Liu

  6. Division of Infectious Diseases, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Kingsley Gideon Kumashie, Shane Kammerman & Laura A. Vella

  7. R&D Center, AbClon Inc., Seoul, Republic of Korea

    Ki-Hyun Kim, Jong-Seo Lee, Yoon Lee & Jong Hoon Kim

  8. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

    Junho Chung

  9. Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Jaryse Harris

  10. College of Pharmacy and Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan, Republic of Korea

    Yong Gu Lee

  11. Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA

    Shan Liu

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Contributions

P.G. and M.R. conceptualized the project, interpreted, discussed results and drafted the manuscript. P.G., A.C., Y.Z., J.H.C., R.P.P., K.-H.K., J.S.L., Y.L., J.H.K., J.C., A.J., I.C., J.H., R.P., M.A., Y.G.L., S.L., J.R., M.W., A.G., O.H.U., S.J.A.H., A.C.B.-L., C.T.S., S.K., O.S., D.L. and P.P. performed experiments. K.G.K., S.K., G.G., L.V., A.C., H.J.B. and L.P. collected, processed or interpreted clinical data. P.G., M.S. and L.C. analyzed sequencing results. J.S. and S.J.S. treated the patients analyzed in this study. M.R. supervised and funded the project. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Marco Ruella.

Ethics declarations

Competing interests

M.R. holds patents related to CD19 CAR T cells. AbClon and Penn have filed a provisional patent application for the CART30 used in this project (patent pending). The University of Pennsylvania has filed a provisional patent application on the BTLA knockout to enhance CART immunotherapies (patent pending). M.R. has served as a consultant for nanoString, BMS, GSK, Bayer, GLG, Sana and AbClon. M.R. receives research funding from AbClon, ONI, Lumicks, nanoString and Beckman Coulter. M.R. is the scientific founder and chair of the scientific advisory board of viTToria biotherapeutics. All other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Phillip Darcy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Analyses of single-cell clusters in HL patient and validation cohorts.

a, UMAP of all clusters amongst merged HL (n = 4) and RLN (n = 5) populations, respectively from exploratory (Penn) and healthy control (Aoki et al.24) scRNA-seq cohorts. The Treg subset is circled. b, UMAP of n = 22 HL patients (Aoki et al.24). c, Ranked ligand–receptor interactions across entire TME of n = 22 HL patients, regardless of cellular subset. Here, to sort interactions, we calculated a RankSum = rank1 + rank2. rank1 is based on the number of times a given interaction was found in the cohort (for example, CellPhoneDB detected a significant interaction between ligand1 and receptor1 in 14 of 22 patients); rank2 is based on the average ‘rank’ value returned by CellPhoneDB, across all patients for the given interaction. d-f, CellPhoneDB enrichment ‘scores’, calculated by cumulatively summing the ‘sig_means’ values of significant ligand–receptor interactions from a specific column (for example, CD4+ TEM versus Treg) across all 22 patients. In d-f, we are not comparing the sum of sig_means to that of RLN, as done in Fig. 1c. d, Total sum of sig_means from interactions of each individual CD4+ and CD8+ T cell subset versus Treg, across all patients. e, Total sum of sig_means from interactions of each individual non-naive CD4 T cell subset (that is, CD4 proliferating, CD4 TCM, CD4 TEM) versus Treg, across all patients. Importantly, other interactions involving TNFRSF14 (that is, CD160-TNFRSF14 (inhibitory) and TNFSF14-TNFRSF14 (stimulatory)) appear enriched as well. f, Total sum of sig_means from interactions of each individual CD4+ and CD8+ T cell subset versus both CD14 and CD16 monocytes. Here, ‘n’ defines the number of biologically independent samples (that is, patients).

Source data

Extended Data Fig. 2 Screening of CAR30 scFv binders.

a, Eleven anti-CD30 CAR constructs were developed and screened for transduction efficiency in primary human T cells from three healthy human donors. CAR30 surface expression was detected using an anti-G4S-linker antibody. HRS3 is a control gold-standard clone. Low transduction efficiency candidates (T1-8, T1-9, T2-2) were not assessed further. b,c, Short-term tumor killing of CAR30 candidates from two donors against (b) CD30-HEK293T (n = 2 technical replicates) and (c) SU-DHL-1 (n = 2 technical replicates). d, IFNγ concentration (ELISA) of supernatant following 24 h coculture with SU-DHL-1 (effector: target = 0.3, n = 2 technical replicates, representative of three independent biological donors). e, Expansion of UTD, HRS3, T1-36, and T1-159, revealing fratricide of T1-36 during manufacturing. f, Assessment of off-target toxicity of lead candidates using CD30- cell lines (OCI-Ly18, Nalm6, and Raji-B). Tumor killing is shown after 48 h against OCI-Ly18 (left) and Nalm6 (middle, effector: target = 2, n = 2 technical replicates), and IFNγ concentration (ELISA) is shown after 24 h against Raji-B (right, effector: target = 1, n = 2 technical replicates). g, Average tumor volumes (± s.d.) of in vivo study comparing T1-159 and HRS3 CAR30 T cells. Briefly, 15 × 106 HDLM-2 cells were inoculated into the right flank of NSG mice, and mice were infused at week 8 with 3 × 105 T1-159 CART30 (n = 4) or HRS3 CART30 (n = 4). Caliper measurements were taken once per week following infusion. h, Schema depicting overall selection process which led to the lead CAR30 construct, T1-159. For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by two sided, multiple unpaired t-test (g). Except for g, ‘n’ defines the number of technical replicates.

Source data

Extended Data Fig. 3 Manufacturing and in vitro validation of BTLA-deleted CART30 cells.

a, Schematic depicting timeline for CRISPR-edited human CAR T cell manufacturing. b, Prediction of off-targeting knockouts from optimized BTLA sgRNA, showing no additional targets. c, Representative analysis of indels in exon 2 of BTLA following CRISPR-Cas9 KO, using the TIDE (Tracking of Indels by DEcomposition) software (https://tide.nki.nl/). d, Expression of T1-159 CAR30 in primary human T cells at day 6 (de-bead) of expansion, measured via flow cytometry using an anti-G4S linker antibody. e, Expansion metrics of T1-159 CART30 for a single donor (ND518), including population doublings, total cells, cell size, and viability. f, Cytotoxicity of WT UTD, BTLA KO UTD, WT CART30, and BTLA KO CART30 against HDLM-2 at 48 h (n = 2 technical replicates per condition). g, Concentration of TNF in supernatant after 24 h of coculture with HDLM-2 (effector: target = 1, n = 3 technical replicates, representative of two independent biological donors). For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by one-way ANOVA with post hoc Tukey tests (g). EP, electroporation.

Source data

Extended Data Fig. 4 BTLA knockout helps CART19 resist exhaustion against DLBCL.

a, Bulk RNA sequencing of FACS-isolated SU-DHL-4 following 48 h coculture with CART19 (effector: target = 0.125, n = 3 technical replicates). b, Luminescence-based in vitro cytotoxicity (72 h) against SU-DHL-4 (n = 2 technical replicates, representative of two independent biological donors). c-g, Chronic antigen exposure (CAE) experiment of WT or BTLA KO CART19 with OCI-Ly18. c, Design of the CAE experiment. 1 × 106 CAR T cells were seeded with OCI-Ly18 in triplicate on day 0 (effector: target = 0.5, n = 3 technical replicates). On days 3, 7, and 11, flow cytometry was performed to determine the absolute counts of CAR T cells per well. OCI-Ly18 was then added to adjust the effector: target ratio to 0.5 in each well. On day 14, cocultures were collected, centrifuged, and washed 2x in PBS + 2% FBS prior to staining and fixing for CyTOF (Standard Biotools MaxPar Kits). d, UMAP projections of CD4+ and CD8+ cells, gated previously on CD3+. Of note, CD4+ cluster 6 and CD8+ cluster 9 are notably, visually distinct between WT and BTLA KO cells. e-f, Distribution (or percent participation) of CD4+ or CD8 + T cells in WT and BTLA KO amongst ten independent clusters. To reiterate, a total of ten clusters were found for each CD4 and CD8 subsets. The differences between WT and BTLA KO cells for CD4+ cluster 6 and CD8+ cluster 9 are statistical. g, Radar plots of MFI for each surface marker within CD4+ cluster 6 and CD8+ cluster 9. h, Raw Luminex assay values used to generate heatmap in Fig. 3h: UTD (n = 5 mice), WT CART19 (n = 6 mice), BTLA KO CART19 (n = 7 mice). For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by DESeq2 with Benjamini-Hochberg correction (a), two-sided, multiple unpaired t-tests (e–g), or one-way ANOVA with post hoc Tukey tests (h).

Source data

Extended Data Fig. 5 The BTLA–HVEM axis does not restrain CAR T cells in B cell leukemia.

a, Luminescence-based in vitro tumor killing of Nalm6 at 48 h of coculture with UTD, CART19, or BTLA KO CART19 (n = 2 technical replicates). b, In vitro tumor killing at 48 h of Nalm6 transduced to constitutively express HVEM (Nalm6-HVEM) (n = 2 technical replicates). c, Design of in vivo Nalm6 study. d, Bioluminescent intensity (± s.d., IVIS Lumina S5) of mice treated with UTD (n = 6 mice), WT CART19 (n = 6 mice), or BTLA KO CART19 (n = 6 mice) following infusion. e, Overall survival of Nalm6-bearing mice. f, ELISA of mouse serum (same n as d) collected on day 7 post-infusion, measuring IFNγ (left) and IL-2 (right). For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by individual one-way ANOVA with post hoc Tukey tests (f), two-sided, multiple unpaired t-test (d), or long-rank Mantel Cox test (e).

Source data

Extended Data Fig. 6 CRISPR-Cas9 KO of primary murine CAR T cells and A20 TME composition.

a, Manufacturing protocol for CRISPR-edited murine CAR T cells b, Knockout efficiency of murine BTLA sgRNA in primary murine CAR T cells following expansion. c, Cell viability (% Live Cells or % propidium-iodide-negative) before and after murine CAR T cell expansion. d-e, A20 subcutaneous tumors (~200 mm3) were established in Foxp3EGFP Balb/c mice, and infused with WT (n = 4 mice) or BTLA KO (n = 4 mice) CD45.1+ Balb/c-derived muCART19. Seven days post-infusion, tumors were resected and analyzed via flow cytometry. d, Gating strategy used to identify and quantify host (CD45.2+) Treg. e, Quantification of various subsets in A20 TME: Treg fraction of host CD4, total host CD11b+ monocytes, fraction of M1 (F4/80+, CD206−) and M2 (F4/80+, CD206+) macrophages, and fraction of neutrophils (Ly6C+, Ly6G+). f, M2 macrophage suppression assay to monitor WT (n = 5 technical replicates) or BTLA KO (n = 5 technical replicates) CART30 tumor killing in the presence of M2-differentiated macrophages (Methods). Luminescence-based killing at 72 h is shown at the given effector:target:macrophage ratios. For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by two-sided, unpaired t-tests (e), or two-way ANOVA (f).

Source data

Extended Data Fig. 7 Single-cell RNA sequencing of tumor-infiltrating CART19.

a,b, Detailed methods and FACS (BD Melody) gating strategies used to process and sort (a) circulating T cells from peripheral blood (Proliferation model in Fig. 5) and (b) tumor infiltrating T cells (TILs, Exhaustion model, this figure). c, Schema of Exhaustion model study design. Briefly, 5 × 106 OCI-Ly18 were implanted into NSG mice on day −14. On day 0, 4 × 106 WT (n = 2 mice) or BTLA KO (n = 2 mice) CART19 were infused via tail vein. On day 11 post-infusion, tumors were resected, minced, and filtered (using both 70-µm and 40-µm filters). RBCs were lysed and 1 µg ml-1 DNase was added to samples to help prevent aggregation. Single-cell suspensions of mice tumors were pooled per condition (n = 2 per arm). TILs were FACS-purified, washed, resuspended in 0.04% BSA in 1× PBS, and loaded onto the 10x Genomics Chromium Controller. After single-cell capture, library preparation, and NGS, data were filtered as described in Methods. d, UMAP of scRNA-seq data from CAR TILs, split by condition (WT, n = 1,730 cells; BTLA KO, n = 1,007 cells). e,f, Defining features of UMAP sections (e), used to label clusters in f. g, Left: DEGs between WT and BTLA KO TILs in the IFNG-high cluster; Right: enriched pathways in BTLA KO CAR TILs within the IFNG-high cluster. h, DEGs between WT and BTLA KO TILs within the IL13-high cluster. Statistical significance was determined using Seurat’s FindMarkers function (that is, two-sided, Wilcoxon rank-sum test with Benjamini–Hochberg correction; g, h). DEGs, differentially expressed genes. Here, ‘n’ defines the number of biologically independent samples.

Source data

Extended Data Fig. 8 Additional confocal images and Grb2 mechanistic data.

a, Graphical abstract of lipid bilayer platform and confocal imaging to colocalize BTLA and SHP-1/SHP-2 within BOX and FOXY CART19. b, Representative confocal images of SHP-1 and BTLA. c. Representative raw images from confocal studies. d,e, Quantification of phosphorylated-Akt (p-Akt) following CD19 and HVEM stimulation. Briefly, human CD19-transduced HVEM+ DM-6 melanoma cells were seeded into culture dishes and allowed to form a stable monolayer for 48 h. 5 × 105 BOX or FOXY CART19 cells were settled onto CD19+ HVEM+ DM-6 monolayers for 0 m, 15 m, and 30 m, and subsequently rinsed off using cold 1× PBS. Cells were then centrifuged, washed twice with cold 1× PBS, and then lysed in buffer (RIPA + protease inhibitor + phosphatase inhibitor). d, Standard western blot was conducted, first to measure p-Akt. Membranes were then stripped, and re-stained for total Akt. Blots were read and quantification was performed using the LI-COR imaging system (Image StudioTM). e, Quantification of p-Akt/Akt for n = 2 independent experiments, using two independent biological donors. f-h, Jurkat-NFAT-GFP reporter cells were transduced with either the BOX CAR19 or FOXY CAR19 construct. f, BOX-Jurkat-CART19 or FOXY-Jurkat-CART19 were cocultured with Nalm6-HVEM in a 1:1 effector:target ratio and monitored each in real-time for the first 15 minutes of coculture via flow cytometry (gated on CD3+ > GFP+). This is a single, representative example. g, BOX-Jurkat-CART19 or FOXY-Jurkat-CART19 were cocultured with either Nalm6 or Nalm6-HVEM (effector: target = 0.25, n = 5 technical replicates per condition), and GFP+ MFI was measured at 6 h using flow cytometry. h, Same experiment and data as in g, but now including the 20 h time point. Statistical significance was determined by two-way ANOVA (g,h).

Source data

Extended Data Fig. 9 Dual inhibition of BTLA and PD-1 in CAR T cells.

a,b, Data from Fig. 2j,m now including PD1 KO condition (n = 8 mice). c. Data from Fig. 3i, now including PD1 KO condition (n = 7 mice). d-k, Dual checkpoint inhibition of BTLA and PD-1 in CAR30 T cells promotes potent tumor control. d. DepMap Expression Score of HVEM and PDL1 in select CD30+ cell lines. e, 72 h killing of UTD or CAR30 T cells with Pembrolizumab (Pembro) against HDLM-2 (effector: target = 0.25, n = 3 technical replicates per condition). f, Proposed schema for dual BTLA/PD-1 inhibition in CAR30 T cells using CRISPR-Cas9-mediated deletion of BTLA and blockade of PD-1 using Pembro g, Design of in vivo study using HDLM-2 and NSG mice (Methods). Experimental arms were UTD (n = 4), UTD + Pembro (n = 4), WT CART30 + Saline (n = 4), WT CART30 + Pembro (n = 6), BTLA KO CART30 + Saline (n = 5), or BTLA KO CART30 + Pembro (n = 6). h, Median bioluminescent intensity (tumor burden) of each condition, with individual mouse curves shown in the background. i, Overall survival. j, Peripheral blood human CD45+ CD3+ counts on day 16 post-CART30 infusion. k, Tumor killing at 48 h of coculture, using WT or BTLA KO CAR30 T cells against KM-H2 (top) or SU-DH-L1 (bottom), in the prescence of either Vehicle (saline) or Pembro (100 μg / mL (effector: target ratio = 0.125, n = 3 technical replicates). l, Expression of BTLA and PD-1 in CD4+ and CD8+ pan-cancer TILs from the online portal of Zheng and colleagues (http://cancer-pku.cn:3838/PanC_T/)41. For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by one-way ANOVA with post hoc Tukey tests (a (week 9 only),e,j,k), or long-rank Mantel Cox test (c,i).

Source data

Extended Data Fig. 10 BTLA-deficient CAR T cells for solid malignancies.

a, Analysis of overall and disease-free survival of all TCGA cohorts based on BTLA expression normalized to CD3E, performed using GEPIA2 (Methods). Cutoffs are defined by quartiles. b,c, TCGA correlates of TNFRSF14/HVEM and PDL1 tumor expression. b, Transcript expression in log2(TPM + 1) of HVEM (top) and PDL1 (bottom) across all TCGA cohorts. c. Disease-free survival of all TCGA cohorts, split either by median HVEM expression (left), or median PDL1 expression (right). d, Disease-free survival of TCGA OV cohort, based on BTLA (left) or PDCD1 (right) RNA expression normalized to CD3E (upper and lower quartile cutoffs). e-g, anti-HER2 studies (clone 4D-5, see Methods). e, GFP Integrated Intensity of SK-OV-3 ovarian cancer cells in coculture with anti-HER2 CAR T cells (effector: target = 0.25, n = 5 technical replicates). f, Disease-free survival of TCGA PRAD cohort, based on BTLA expression normalized to CD3E (median cutoff). g, Cell Index (impedence) of PC-3 prostate cancer cells in coculture with anti-HER2 CAR T cells (effector: target = 0.5, n = 3 technical replicates). h, Knockout of HVEM from PC-3 eliminates benefit of BTLA KO in anti-HER2 CAR T cells. Here, 4,000 WT or HVEM-deficient CBG+ PC-3 cells were seeded 24 hr prior to the addition of anti-HER2 CAR T cells (n = 3 technical replicates). Luminescence was measured at 72 h of coculture. i, OS of TCGA PAAD cohort, based on BTLA expression normalized to CD3E (median cutoff). j, Low basal HVEM expression on AsPC-1 pancreatic cancer cells. k, Luminescence-based cytotoxicity studies of anti-mesothelin CAR (clone M5, see NCT03323944) T cells (that is, CARTM5) against mesothelin+ CBG+ HVEM-low AsPC-1 cells. Here, 4,000 AsPC-1 cells were seeded 24 h before the addition of WT or BTLA KO CARTM5 cells at various effector to target ratios (n = 3 technical replicates). Measurements were taken at 48 h and 72 h of coculture and show no benefit of BTLA KO in this setting. l, Cell Index (impedence) of B16-muCD19 cells cocultured with high doses of UTD, WT muCART19, or BTLA KO muCART19 (n = 3 technical replicates, see Methods). m-p, In vivo B16-muCD19 melanoma study (Methods). m,n, Tumor volumes following infusion of WT (n = 8 mice, m) or BTLA KO (n = 9 mice, n) murine CART19 cells. o, Tumor sizes on day 14 following CAR T cell infusion (WT, n = 8; BTLA KO, n = 9) p, Overall survival. For all bar plots, data are presented as mean ± s.d. Statistical significance was determined by one-way ANOVA with post hoc Tukey tests (e,g,l at endpoints), two-way ANOVA (k), two-sided unpaired t-tests (h,o), or long-rank Mantel Cox test (c,d,f,i,p).

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

HL patient metadata (from Fig. 1).

Supplementary Table 2

Infusion product RNA-seq correlates (from Fig. 7).

Supplementary Table 3

List of antibodies used in this manuscript.

Source data

Statistical source data.

Source Data Figs. 1-7 and Source Data Extended Data Figs. 1-10

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Guruprasad, P., Carturan, A., Zhang, Y. et al. The BTLA–HVEM axis restricts CAR T cell efficacy in cancer. Nat Immunol 25, 1020–1032 (2024). https://doi.org/10.1038/s41590-024-01847-4

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