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Targeting T cell checkpoints 41BB and LAG3 and myeloid cell CXCR1/CXCR2 results in antitumor immunity and durable response in pancreatic cancer

Nature Cancer volume 4, pages 62–80 (2023)Cite this article

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is considered non-immunogenic, with trials showing its recalcitrance to PD1 and CTLA4 immune checkpoint therapies (ICTs). Here, we sought to systematically characterize the mechanisms underlying de novo ICT resistance and to identify effective therapeutic options for PDAC. We report that agonist 41BB and antagonist LAG3 ICT alone and in combination, increased survival and antitumor immunity, characterized by modulating T cell subsets with antitumor activity, increased T cell clonality and diversification, decreased immunosuppressive myeloid cells and increased antigen presentation/decreased immunosuppressive capability of myeloid cells. Translational analyses confirmed the expression of 41BB and LAG3 in human PDAC. Since single and dual ICTs were not curative, T cell-activating ICTs were combined with a CXCR1/2 inhibitor targeting immunosuppressive myeloid cells. Triple therapy resulted in durable complete responses. Given similar profiles in human PDAC and the availability of these agents for clinical testing, our findings provide a testable hypothesis for this lethal disease.

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Fig. 1: Prominent infiltration of myeloid immunosuppressive cells in iKRAS tumors.
Fig. 2: Prominent infiltration of myeloid immunosuppressive cells in human PDAC tumors.
Fig. 3: Heterogeneity of myeloid cells in three iKRAS PDAC tumors identified by single-cell gene expression profiling.
Fig. 4: Dysfunctional phenotype of T cells in three iKRAS PDAC tumors identified by single-cell gene expression profiling.
Fig. 5: Efficacy of ICT and treatment effects on the immune microenvironment.
Fig. 6: Effects of ICT treatment on the immune microenvironment.
Fig. 7: Efficacy of targeted therapy directed against Cxcr1/2 and treatment effects on the immune microenvironment.
Fig. 8: Efficacy of ICT in combination with targeted therapy directed against Cxcr1/2 and treatment effects on the immune microenvironment.

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Data availability

Murine scRNA-seq and TCR sequencing data supporting the findings of this study have been deposited in the Sequence Read Archive under BioProject accession code PRJNA496487. Human PDAC genomic data were derived from the TCGA Research Network (http://cancergenome.nih.gov) and ICGC Research Network (https://dcc.icgc.org). Human PDAC scRNA-seq data were derived from the Genome Sequence Archive (accession codes CRA001160 and GSE155698). All of the other data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank the Small Animal Imaging Facility, Histology Core, Flow Cytometry and Cellular Imaging Core and Single Cell Genomics Core at the MD Anderson Cancer Center for assistance with these studies (NCI P30CA16672; CPRIT RP180684). We thank the members of the Multiplex Immunofluorescence and Image Analysis Laboratory in the Department of Translational Molecular Pathology for assistance with multiplex immunofluorescence and image analysis. We thank the Biospecimen Repository and Histopathology Service, Flow Cytometry and Cell Sorting Shared Resource, Biostatistics Shared Resource and Molecular Imaging Core at the Rutgers Cancer Institute of New Jersey (NCI P30CA072720). These studies were supported by NIH NCI P01 CA117969 (to R.A.D.), the Elsa Pardee Foundation Award, the Advanced Scholars Program, the Eleanor Russo Fund for Pancreatic Research, the Ralph A. Loveys Family Charitable Foundation, The Cultural & Charitable Club of Somerset Run and the New Jersey Health Foundation Award (to P.G.), NIH NCI R01CA240526 and R01CA236864 (to N.E.N.), NIH NCI R01CA231349 and 1R01CA258540 (to Y.A.W.) and NIH NCI R01CA220236 and P50CA221707, the Sheikh Khalifa Bin Zayed Foundation and MD Anderson’s Pancreatic Cancer Moon Shot (to A.M.). We thank V. Kulkarni for assistance with figure preparation.

Author information

Authors and Affiliations

  1. Department of Medical Oncology, Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA

    Pat Gulhati

  2. Department of Gastrointestinal Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Pat Gulhati

  3. Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Aislyn Schalck, Emi Sei, Michael Kim & Nicholas E. Navin

  4. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Shan Jiang, Xiaoying Shang, Pingping Hou, Jincheng Han, Pingna Deng, Denise J. Spring, Y. Alan Wang & Ronald A. DePinho

  5. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Chang-Jiun Wu & Jun Li

  6. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Sharia Hernandez Ruiz, Luisa Solis Soto & Edwin Parra

  7. Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Haoqiang Ying

  8. Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

    Prasenjit Dey

  9. Syntrix Pharmaceuticals, Auburn, WA, USA

    Dean Y. Maeda & John A. Zebala

  10. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Michael Kim

  11. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Huamin Wang & Anirban Maitra

  12. Department of Biostatistics, Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA

    Dirk Moore

  13. Department of Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Karen Clise-Dwyer

  14. Brown Center for Immunotherapy, Indiana University School of Medicine, Indianapolis, IN, USA

    Y. Alan Wang

  15. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

    Y. Alan Wang

  16. Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, USA

    Y. Alan Wang

  17. Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA

    Y. Alan Wang

Authors
  1. Pat Gulhati
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  2. Aislyn Schalck
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Contributions

P.G. and R.A.D. conceived of and designed the study. P.G. performed most of the experiments and wrote the manuscript. A.S., E.S. and N.E.N. assisted with the design, performance and data analysis for the mouse scRNA-seq experiments. S.J. and X.S. assisted with the in vivo experiments and CyTOF. C.-J.W. and J.L. provided bioinformatics support for TCGA/ICGC and human scRNA-seq analysis. S.H.R., L.S.S. and E.P. assisted with the IHC and immunofluorescence of human PDAC specimens. P.H., H.Y., J.H., P. Dey and P. Deng assisted with the mouse colonies and in vivo experiments. D.Y.M. and J.A.Z. provided technical assistance with SX-682. D.J.S. assisted with manuscript editing, figure revisions and data review. M.K. and H.W. assisted with the procurement of human specimens. A.M. assisted with the procurement of human specimens and contributed expertise regarding PDAC and immunotherapy. K.C.-D. assisted with the flow cytometry experiments. D.M. assisted with the statistical analysis. All of the authors reviewed and edited the manuscript. Y.A.W. and R.A.D. supervised the study.

Corresponding authors

Correspondence to Y. Alan Wang or Ronald A. DePinho.

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Competing interests

R.A.D. is a founder, advisor and/or director of Tvardi Therapeutics, Asylia Therapeutics, Stellanova Therapeutics, Nirogy Therapeutics and Sporos Bioventures. J.A.Z. is the President and Chief Executive Officer at Syntrix Pharmaceuticals. D.Y.M. is the Director of Medicinal Chemistry and Preclinical Development at Syntrix Pharmaceuticals. A.M. receives royalties from Cosmos Wisdom Biotechnology and Thrive Earlier Detection, an Exact Sciences company. A.M. is also a consultant for Freenome and Tezcat Biotechnology. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Prominent infiltration of myeloid immunosuppressive cells in iKRAS tumors.

A. PDAC tumor development in syngeneic mouse model with representative images of tumor detected by bioluminescence, PET/CT and MRI at indicated timepoints. B. Tumor volume measured by MRI at indicated timepoints (top) and Kaplan-Meier curve depicting overall survival (bottom) for untreated iKRAS tumor bearing mice (n = 10 mice). C. Representative images of normal pancreas, orthotopic and autochthonous (GEMM) iKRAS tumors with H&E, Masson Trichrome, smooth muscle actin (SMA) and vimentin staining. Scale bars: 100 µm. D. Representative H&E images of iKRAS tumors invading into adjacent lymph nodes (left = 4x magnification, right=20x magnification). E. Representative coronal and axial MRI images of iKRAS tumor invading into duodenum. F. Representative images of normal pancreas and human PDAC tumors with SMA, Vimentin and CD45 staining. Scale bars: 100 µm. Red arrow indicates positively stained cells. G. Percentage of granulocytic (CD45+CD11b+Ly6G+Ly6C−) and monocytic MDSCs (CD45+CD11b+Ly6G−Ly6C+) within syngeneic iKRAS tumors (n = 10 tumors) assessed by CyTOF at 4 weeks after initial tumor detection. Two-sided Student’s t-test. H. Representative images (bottom) of normal pancreas, orthotopic and autochthonous (GEMM) iKRAS tumors with indicated staining. n = 6 biological replicates. Scale bars: 100 µm. The bar graph (top) shows quantification of each cell type as analyzed by IHC. Two-sided Student’s t-test. I. Representative images of normal pancreas and orthotopic iKRAS tumors with indicated staining. Scale bars: 100 µm. J. Percentage of Treg (CD45+CD3+TCRβ+CD4+FoxP3+) among CD4+ T cells within syngeneic iKRAS tumors (n = 10 tumors) assessed by CyTOF at 4 weeks after initial tumor detection. Two-sided Student’s t-test. K. Representative images of normal pancreas and human PDAC tumors with indicated staining. Scale bars: 100 µm. Red arrow indicates positively stained cells. Data in G,H,J are presented as mean ± s.e.m.

Source data

Extended Data Fig. 2 Prominent infiltration of myeloid immunosuppressive cells in human PDAC tumors.

A. Representative images of normal pancreas and human PDAC tumors with indicated staining. Scale bars: 100 µm. Red arrow indicates positively stained cells. B. Clustering of human TCGA PDAC samples (n = 178 patients) into MDSC-high, MDSC-low and MDSC-medium groups using a 39-gene MDSC signature13. C. CIBERSORTx quantification of monocyte/macrophage subset fraction in human PDAC samples; TCGA (n = 178 patients) and ICGC-AU (n = 92 patients). D. Representative images of normal pancreas and human PDAC tumors with indicated staining. Scale bars: 100 µm. Red arrow indicates positively stained cells. E. Kaplan-Meier plot depicting overall survival of TCGA PDAC patients (n = 178 patients) grouped by the gene expression signatures of C1q+ TAM (top) and Spp1+ TAM (bottom).

Source data

Extended Data Fig. 3 Heterogeneity of myeloid cells in iKRAS PDAC tumors identified by single cell gene expression profiling.

A. UMAP of all live CD45+ cells used for scRNA-seq analysis of untreated iKRAS tumors (n = 4,080 cells). B. Representative genes and functional markers used for identification of immune cell clusters. C. Heatmap of six immune cell clusters with unique signature genes. D. Representative genes and functional markers used for identification of myeloid cell clusters. E. Heatmap of myeloid cell clusters with unique signature genes. F. Representative genes and functional markers used for identification of dendritic cell clusters.

Extended Data Fig. 4 Dysfunctional phenotype of T cells in iKRAS PDAC tumors identified by single cell gene expression profiling.

A. Representative genes and functional markers used for identification of T cell clusters. B. Heatmap of two CD4+ and four CD8+ T cell clusters with unique signature genes. C. Cell cycle scoring for two CD4+ and four CD8+ T cell clusters. D. Relative expression of select genes in CD8+ T cells as a function of pseudotime from Monocle2 inferred trajectory. Each point corresponds to a single cell, colored by CD8+ T cell cluster. Lines represent average expression at that location in the trajectory. E. Quantification of immune checkpoint expression on infiltrating CD4+ and CD8+ T cells in iKRAS tumors (n = 3 biological replicates), assessed by flow cytometry and analyzed by FlowJo.

Source data

Extended Data Fig. 5 Efficacy of immune checkpoint therapy (ICT) and treatment effects on immune microenvironment.

A. Treatment schedule and monitoring procedures for preclinical trials to evaluate effect of ICT on iKRAS PDAC bearing mice. B. Heatmap of immune checkpoint expression on T cells after 4−week treatment with control, anti-PD1 or anti-CTLA4 antibody (n = 3 mice/ group). C. UMAP demonstrating cell types in single-cell RNA sequencing of human PDAC samples from Peng et al.26 and Steele et al.10 (left), and expression of LAG3 and 41BB (TNFRSF9) on T cells (right). D. UMAP of all live CD45+ cells used for scRNA-seq analysis of iKRAS tumors treated with control, anti-PD1, anti-CTLA4, anti-41BB, anti-LAG3, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) treatment (n = 3 mice/group). E. UMAP projection of immune cell clusters (top) and cells with TCR detected (bottom). F. Violin plots displaying relative expression of representative genes and functional markers used for identification of immune cell clusters.

Extended Data Fig. 6 Efficacy of immune checkpoint therapy (ICT) and treatment effects on immune microenvironment.

A. Heatmap of six immune cell clusters with unique signature genes. B. UMAP projection of T cell clusters (top) and violin plots displaying relative expression of representative genes and functional markers used for identification of T cell clusters (bottom). C. Heatmap of ten T cell clusters with unique signature genes. D. UMAP projection of neutrophil/granulocyte clusters (top) and violin plots displaying relative expression of representative genes and functional markers used for identification of neutrophil/granulocyte clusters (bottom).

Extended Data Fig. 7 Efficacy of immune checkpoint therapy (ICT) and treatment effects on immune microenvironment.

A. Heatmap of five neutrophil/granulocyte clusters with unique signature genes. B. UMAP projection of monocyte/macrophage clusters (top) and violin plots displaying relative expression of representative genes and functional markers used for identification of monocyte/macrophage clusters (bottom). C. Heatmap of five monocyte/macrophage clusters with unique signature genes. D. Proportion of immune cell subtypes in single-cell sequencing analysis of established iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, anti-PD1, anti-CTLA4, anti-41BB or anti-LAG3 antibody for 4 weeks (n = 3 tumors/group).

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Extended Data Fig. 8 Effects of immune checkpoint therapy (ICT) treatment on immune microenvironment.

A. Proportion of T cell subtypes in scRNA-seq analysis of established iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, anti-PD1, anti-CTLA4, anti-41BB or anti-LAG3 antibody for 4 weeks (n = 3 tumors/group). B. Top gene ontologies from GSEA of differential expression in T cells from anti-41BB and control antibody treated mice (n = 3 mice/group). C. Circos plots of T cell receptor clonotype frequencies and expression states of CD8+ T cells in iKRAS tumors after treatment with control (left) and anti-41BB antibody (right) for 4 weeks. Outer histogram is the frequency of each clonotype. Inner bars show the fraction of cells of particular clonotype in each expression state (colors correspond to the clusters in Extended Data Fig. 8a). Inner dendrograms are the hierarchical clustering of gene expression centroids for each clonotype. D. Multiple-testing corrected 95% binomial confidence intervals on the probability of a cell in each treatment group containing a TCR CD3R sequence which overlaps that of another cluster. (*p < 0.05) E. Violin plots showing CCR7 expression in CD4+, CD8+ and CD4-CD8− T cells. (*p < 0.05 two-sided unpaired Wilcox test) F. Proportion of CD4+, CD8+ and CD4−CD8− T cells with expression of CCR7 (left) and IL2Rβ (right). G. Expression of genes and functional markers on CD3+CD4−CD8− T cells. H. Violin plots showing expression of Stat6, Socs3 and Il1β among myeloid cells from control and anti-LAG3 antibody-treated tumors (n = 3 mice/group). (*p < 0.05 two-sided unpaired Wilcox test) I. Violin plots showing expression of Cxcl10, Stat1, Il10, Mrc1 and Socs3 among myeloid cells from control and anti-41BB antibody-treated tumors (n = 3 mice/group). (*p < 0.05 two-sided unpaired Wilcox test).

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Extended Data Fig. 9 Efficacy of targeted therapy directed against Cxcr1/2 and treatment effects on immune microenvironment.

A. Kaplan-Meier plot depicting overall survival differences between patients with MDSC-high vs. MDSC-low signatures based on clustering of human TCGA PDAC samples (n = 178 patients) shown in Extended Data Fig. 2b. B. Representative images (left) of established iKRAS tumors treated with control and anti-Gr1 neutralizing antibody for 4 weeks with indicated staining. Scale bars: 100 µm. The bar graph (right) shows quantification of each cell type as analyzed by IHC. n = 6 biological replicates. Two-sided Student’s t-test. C. Tumor volume after 4 weeks of treatment with control or anti-Gr1 neutralizing antibody in mice bearing established (tumor volume ~250mm3 prior to treatment initiation) orthotopic iKRAS tumors (n = 10 mice/group). Two-sided Student’s t-test. D. Expression of Cxcr2 on granulocytic MDSCs in untreated iKRAS tumors, assessed by flow cytometry and analyzed by FlowJo (n = 3 tumors). E. Representative images of human PDAC tumors with indicated staining. Scale bars: 100 µm. Red arrow indicates positively stained cells in the same area of a core specimen. F. UMAP demonstrating cell types in single-cell RNA sequencing of human PDAC samples from Steele et al.10 with the expression of CXCR1 and CXCR2 on granulocytes/neutrophils and expression the of CSF1R, CCR2 and TREM2 on monocytes/macrophages. G. Migration of MDSCs toward conditioned medium from iKRAS tumor cells treated with control or SX-682 (n = 3 biological replicates). Student’s t-test. H. Tumor volume after 4 weeks of treatment with control or SX-682 in mice bearing established orthotopic iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) (n = 10 mice/group). Two-sided Student’s t-test. I. Stratification of infiltrating CD4+ and CD8+ T cells as naive (CD44lowCD62Lhigh), central memory (CD44highCD62Lhigh) and effector memory (CD44highCD62Llow), in established iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks assessed by flow cytometry and analyzed by FlowJo (n = 3 biological replicates). Two-sided Student’s t-test. J. Quantification of total tumor associated macrophages (TAM) and dendritic cells (DC) in established iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks assessed by flow cytometry and analyzed by FlowJo (n = 3 biological replicates). Two-sided Student’s t-test. K. Expression of Cxcr2 on myeloid cells in established iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks assessed by flow cytometry and analyzed by FlowJo (n = 3 biological replicates). L. Representative images (left) of control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) treated iKRAS tumors with indicated staining. Scale bars: 100 µm. The bar graphs (right) show quantification of each cell type as analyzed by IHC. n = 6 biological replicates. Two-sided Student’s t-test. M. Quantification of change in the proportion of cells in cluster M_c2 as a proportion of total monocyte/macrophage cells in scRNA-seq analysis of iKRAS tumors following treatment with control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks (n = 3 mice/group). (*p < 0.05 mixed effect model) N. Tumor volume of mice bearing established orthotopic iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control, SX-682 or SX-682 with CD8 T cell depleting antibody (n = 10 mice/group). Two-sided Student’s t-test. Data in D,G,I,J,M are presented as mean ± s.e.m.

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Extended Data Fig. 10 Efficacy of ICT in combination with targeted therapy directed against Cxcr1/2 and treatment effects on immune microenvironment.

A. Tumor volume of mice bearing established orthotopic iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control or anti-LAG3 + anti-41BB antibodies or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks (n = 10 mice/group). Two-sided Student’s t-test. B. Tumor volume of mice bearing established orthotopic iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control or anti-PD1 + anti-CTLA4 antibodies or SX-682 or anti-PD1 + anti-CTLA4 + SX-682 for 4 weeks (n = 10 mice/group). Two-sided Student’s t-test. C. Body weight of mice (top left), before (pre-treatment), during (2 weeks) and after (4 weeks) treatment with control, SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) for 4 weeks (n = 4 biological replicates). Mouse toxicity tests including creatinine, blood urea nitrogen (BUN), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin and alkaline phosphatase in the indicated treatment groups (n = 4 biological replicates). Representative images of H&E staining (middle) of the lung, heart, liver, kidney and spleen in the indicated treatment groups (n = 4 mice/group). Inset (bottom) shows representative H&E staining of liver tissues in the indicated treatment groups at higher magnification. D. CyTOF analysis of tumors from syngeneic iKRAS 2 and iKRAS 3 tumor bearing mice with equivalent tumor volume (~1000mm3) (n = 10 tumors/group). E. Quantification of tumor infiltrating CD45+ cells in syngeneic iKRAS 2 and iKRAS 3 tumors with equivalent tumor volume (~1000mm3) assessed by CyTOF (n = 10 tumors/group). F. Overall survival of mice bearing established orthotopic iKRAS 2 and iKRAS 3 tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control or anti-LAG3 + anti-41BB + SX-682 for 4 weeks (n = 10 mice/group). Statistical differences were identified by Kaplan-Meier with log-rank test. G. Treatment schedule and monitoring procedures for preclinical trial to evaluate overall survival of mice bearing established autochthonous iKRAS tumors (tumor volume ~250mm3 prior to treatment initiation) treated with control or anti-PD1 + anti-CTLA4 antibodies or anti-LAG3 + anti-41BB antibodies or SX-682 or combination (anti-LAG3 + anti-41BB + SX-682) (n = 10 mice/group). Animals in the ‘extended’ treatment group received treatment with the combination regimen for 6 months or until death. Data in E are presented as mean ± s.e.m.

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Gulhati, P., Schalck, A., Jiang, S. et al. Targeting T cell checkpoints 41BB and LAG3 and myeloid cell CXCR1/CXCR2 results in antitumor immunity and durable response in pancreatic cancer. Nat Cancer 4, 62–80 (2023). https://doi.org/10.1038/s43018-022-00500-z

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