Journal Articles

KIR-based inhibitory CARs overcome CAR-NK cell trogocytosis-mediated fratricide and tumor escape

Trogocytosis is an active process that transfers surface material from targeted to effector cells. Using multiple in vivo tumor models and clinical data, we report that chimeric antigen receptor (CAR) activation in natural killer (NK) cells promoted transfer of the CAR cognate antigen from tumor to NK cells, resulting in (1) lower tumor antigen density, thus impairing the ability of CAR-NK cells to engage with their target, and (2) induced self-recognition and continuous CAR-mediated engagement, resulting in fratricide of trogocytic antigen-expressing NK cells (NK(TROG+)) and NK cell hyporesponsiveness. This phenomenon could be offset by a dual-CAR system incorporating both an activating CAR against the cognate tumor antigen and an NK self-recognizing inhibitory CAR that transferred a 'don't kill me' signal to NK cells upon engagement with their TROG(+) siblings. This system prevented trogocytic antigen-mediated fratricide, while sparing activating CAR signaling against the tumor antigen, and resulted in enhanced CAR-NK cell activity.

Author Info: (1) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. UTHealth Graduate School of Biomedical Scienc

Author Info: (1) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (3) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (4) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (5) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (6) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (7) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Human Genome and Stem Cell Research Center, Department of Genetics and Evolutionary Biology, Biosciences Institute, University of Sao Paulo, Sao Paulo, Brazil. Department of Stem Cell Transplantation and Cellular Therapy, Hospital Israelita Albert Einstein, Sao Paulo, Brazil. (8) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (9) Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA. (10) Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA. (11) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (12) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (13) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (14) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (15) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (16) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (17) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (18) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (19) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (20) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Department of Medicine III: Hematology and Oncology, Technical University of Munich, Munich, Germany. (21) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (22) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (23) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (24) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (25) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (26) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (27) Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (28) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (29) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (30) Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA. (31) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (32) Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. KRezvani@mdanderson.org.

PD-1-cis IL-2R agonism yields better effectors from stem-like CD8+ T cells

Expansion and differentiation of antigen-experienced PD-1(+)TCF-1(+) stem-like CD8(+) T cells into effector cells is critical for the success of immunotherapies based on PD-1 blockade(1-4). Hashimoto et al. have shown that, in chronic infections, administration of the cytokine interleukin (IL)-2 triggers an alternative differentiation path of stem-like T cells towards a distinct population of 'better effector' CD8(+) T cells similar to those generated in an acute infection(5). IL-2 binding to the IL-2 receptor _-chain (CD25) was essential in triggering this alternative differentiation path and expanding better effectors with distinct transcriptional and epigenetic profiles. However, constitutive expression of CD25 on regulatory T cells and some endothelial cells also contributes to unwanted systemic effects from IL-2 therapy. Therefore, engineered IL-2 receptor _- and _-chain (IL-2R__)-biased agonists are currently being developed(6-10). Here we show that IL-2R__-biased agonists are unable to preferentially expand better effector T cells in cancer models and describe PD1-IL2v, a new immunocytokine that overcomes the need for CD25 binding by docking in cis to PD-1. Cis binding of PD1-IL2v to PD-1 and IL-2R__ on the same cell recovers the ability to differentiate stem-like CD8(+) T cells into better effectors in the absence of CD25 binding in both chronic infection and cancer models and provides superior efficacy. By contrast, PD-1- or PD-L1-blocking antibodies alone, or their combination with clinically relevant doses of non-PD-1-targeted IL2v, cannot expand this unique subset of better effector T cells and instead lead to the accumulation of terminally differentiated, exhausted T cells. These findings provide the basis for the development of a new generation of PD-1 cis-targeted IL-2R agonists with enhanced therapeutic potential for the treatment of cancer and chronic infections.

Author Info: (1) Roche Innovation Center Zurich, Schlieren, Switzerland. (2) Roche Innovation Center Zurich, Schlieren, Switzerland. (3) Emory Vaccine Center and Department of Microbiology and

Author Info: (1) Roche Innovation Center Zurich, Schlieren, Switzerland. (2) Roche Innovation Center Zurich, Schlieren, Switzerland. (3) Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA. (4) Roche Innovation Center Zurich, Schlieren, Switzerland. (5) Roche Innovation Center Basel, Basel, Switzerland. (6) Roche Innovation Center Zurich, Schlieren, Switzerland. (7) Roche Innovation Center Zurich, Schlieren, Switzerland. (8) Roche Innovation Center Zurich, Schlieren, Switzerland. (9) Roche Innovation Center Zurich, Schlieren, Switzerland. (10) Roche Innovation Center Zurich, Schlieren, Switzerland. (11) Roche Innovation Center Zurich, Schlieren, Switzerland. (12) Roche Innovation Center Zurich, Schlieren, Switzerland. (13) Roche Innovation Center Zurich, Schlieren, Switzerland. (14) Roche Innovation Center Zurich, Schlieren, Switzerland. (15) Roche Innovation Center Zurich, Schlieren, Switzerland. (16) Roche Innovation Center Zurich, Schlieren, Switzerland. (17) Roche Innovation Center Munich, Penzberg, Germany. (18) Roche Innovation Center Zurich, Schlieren, Switzerland. (19) Roche Innovation Center Zurich, Schlieren, Switzerland. (20) Roche Innovation Center Zurich, Schlieren, Switzerland. (21) Roche Innovation Center Zurich, Schlieren, Switzerland. (22) Roche Innovation Center Zurich, Schlieren, Switzerland. (23) Roche Innovation Center Zurich, Schlieren, Switzerland. (24) Roche Innovation Center Zurich, Schlieren, Switzerland. (25) Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, EPFL, Lausanne, Switzerland. Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland. (26) Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, EPFL, Lausanne, Switzerland. Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland. Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland. Agora Translational Cancer Research Center, Lausanne, Switzerland. (27) Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, EPFL, Lausanne, Switzerland. Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland. Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland. Agora Translational Cancer Research Center, Lausanne, Switzerland. (28) Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA. Department of Urology, Emory University School of Medicine, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA. (29) Roche Innovation Center Munich, Penzberg, Germany. (30) Roche Innovation Center Zurich, Schlieren, Switzerland. (31) Roche Innovation Center Munich, Penzberg, Germany. (32) Roche Innovation Center Basel, Basel, Switzerland. (33) Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA. (34) Roche Innovation Center Zurich, Schlieren, Switzerland. christian.klein.ck1@roche.com. (35) Roche Innovation Center Zurich, Schlieren, Switzerland. pablo.umana@roche.com.

Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade

Liu et al. demonstrated that CXCL13 expression can effectively discriminate tumor-reactive T cells from bystander CD8+ T cell clones within tumors. Tumor-reactive CXCL13+CD8+ T cells showed precursor-like and terminally differentiated phenotypes, and their abundance significantly correlated with response to ICB across multiple cancer types. A similar correlation was observed with tumor-reactive blood cells. Higher CXCL13+CD4+ T cells also correlated with favorable response to ICB, and simultaneous measurement of CXCL13+ CD8+ and CD4+ T cell abundance achieved an overall predictive accuracy of ≥90% in multiple datasets.

Contributed by Shishir Pant

Liu et al. demonstrated that CXCL13 expression can effectively discriminate tumor-reactive T cells from bystander CD8+ T cell clones within tumors. Tumor-reactive CXCL13+CD8+ T cells showed precursor-like and terminally differentiated phenotypes, and their abundance significantly correlated with response to ICB across multiple cancer types. A similar correlation was observed with tumor-reactive blood cells. Higher CXCL13+CD4+ T cells also correlated with favorable response to ICB, and simultaneous measurement of CXCL13+ CD8+ and CD4+ T cell abundance achieved an overall predictive accuracy of ≥90% in multiple datasets.

Contributed by Shishir Pant

ABSTRACT: Immune-checkpoint blockade (ICB) therapies represent a paradigm shift in the treatment of human cancers; however, it remains incompletely understood how tumor-reactive T cells respond to ICB across tumor types. Here, we demonstrate that measuring CXCL13 expression could effectively identify both precursor and terminally differentiated tumor-reactive CD8(+) T cells within tumors. Applying this approach, we performed meta-analyses of published single-cell data for CXCL13(+)CD8(+) T cells in 225 samples from 102 patients treated with ICB across five cancer types. We found that CXCL13(+)CD8(+) T cells were correlated with favorable responses to ICB, and the treatment further increased such cells in responsive tumors. In addition, CXCL13(+) tumor-reactive subsets exhibited variable responses to ICB in distinct contexts, likely due to different degrees of exhaustion-related immunosuppression. Our integrated analyses provide insights into mechanisms underlying ICB and suggest that bolstering precursor tumor-reactive CD8(+) T cells might provide an effective therapeutic approach to improve cancer treatment.

Author Info: (1) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (2) Peking-Tsinghua Cen

Author Info: (1) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (2) Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. (3) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (4) Analytical Biosciences Limited, Beijing, China. (5) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. zemin@pku.edu.cn. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. zemin@pku.edu.cn. Changping Laboratory, Beijing, China. zemin@pku.edu.cn.

High-throughput mutagenesis identifies mutations and RNA-binding proteins controlling CD19 splicing and CART-19 therapy resistance

To better understand and potentially predict CD19 loss-mediated resistance to CAR T cell therapy in patients with B-ALL, Cortés-López, Schulz, and Enculescu et al. investigated the effects of intronic and exonic mutations in the CD19 gene region encompassing exons 1-3 on splicing. They identified 200 single-point mutations that alter CD19 splicing and nearly 100 previously unknown splice isoforms, many of which emerged from cryptic (not observed in unmutated) splice sites and which likely code for non-functional CD19 molecules. They also identified 119 previously reported and novel RNA-binding proteins, including SF3B4 and PTBP1, that contribute to proper CD19 splicing.

Contributed by Lauren Hitchings

To better understand and potentially predict CD19 loss-mediated resistance to CAR T cell therapy in patients with B-ALL, Cortés-López, Schulz, and Enculescu et al. investigated the effects of intronic and exonic mutations in the CD19 gene region encompassing exons 1-3 on splicing. They identified 200 single-point mutations that alter CD19 splicing and nearly 100 previously unknown splice isoforms, many of which emerged from cryptic (not observed in unmutated) splice sites and which likely code for non-functional CD19 molecules. They also identified 119 previously reported and novel RNA-binding proteins, including SF3B4 and PTBP1, that contribute to proper CD19 splicing.

Contributed by Lauren Hitchings

ABSTRACT: Following CART-19 immunotherapy for B-cell acute lymphoblastic leukaemia (B-ALL), many patients relapse due to loss of the cognate CD19 epitope. Since epitope loss can be caused by aberrant CD19 exon 2 processing, we herein investigate the regulatory code that controls CD19 splicing. We combine high-throughput mutagenesis with mathematical modelling to quantitatively disentangle the effects of all mutations in the region comprising CD19 exons 1-3. Thereupon, we identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to developing CART-19 resistance. Furthermore, we report almost 100 previously unknown splice isoforms that emerge from cryptic splice sites and likely encode non-functional CD19 proteins. We further identify cis-regulatory elements and trans-acting RNA-binding proteins that control CD19 splicing (e.g., PTBP1 and SF3B4) and validate that loss of these factors leads to pervasive CD19 mis-splicing. Our dataset represents a comprehensive resource for identifying predictive biomarkers for CART-19 therapy.

Author Info: (1) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (2) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (3) Institute of M

Author Info: (1) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (2) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (3) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (4) Department of Pediatric Hematology/Oncology, Center for Pediatric and Adolescent Medicine, University Medical Center of the Johannes Gutenberg University Mainz, 55131, Mainz, Germany. University Cancer Center (UCT), University Medical Center of the Johannes Gutenberg University Mainz, 55131, Mainz, Germany. German Cancer Consortium (DKTK), site Frankfurt/Mainz, Germany, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. (5) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (6) Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA. Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA. (7) Division of Cancer Pathobiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. (8) Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Allmandring 30E, 70569, Stuttgart, Germany. (9) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (10) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (11) Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Allmandring 30E, 70569, Stuttgart, Germany. (12) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (13) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (14) Division of Cancer Pathobiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. (15) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. (16) Department of Pediatric Hematology/Oncology, Center for Pediatric and Adolescent Medicine, University Medical Center of the Johannes Gutenberg University Mainz, 55131, Mainz, Germany. University Cancer Center (UCT), University Medical Center of the Johannes Gutenberg University Mainz, 55131, Mainz, Germany. German Cancer Consortium (DKTK), site Frankfurt/Mainz, Germany, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. (17) Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA. (18) Division of Cancer Pathobiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. Department of Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA. (19) Buchmann Institute for Molecular Life Sciences (BMLS), Max-von-Laue-Str. 15, 60438, Frankfurt, Germany. kathi.zarnack@bmls.de. Faculty Biological Sciences, Goethe University Frankfurt, Max-von-Laue-Str. 15, 60438, Frankfurt, Germany. kathi.zarnack@bmls.de. (20) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. legewie@ibmg.uni-stuttgart.de. Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Allmandring 30E, 70569, Stuttgart, Germany. legewie@ibmg.uni-stuttgart.de. Stuttgart Research Center for Systems Biology (SRCSB), University of Stuttgart, Stuttgart, Germany. legewie@ibmg.uni-stuttgart.de. (21) Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. j.koenig@imb-mainz.de.

High-Throughput, Quantitative Analysis of Peptide-Exchanged MHCI Complexes by Native Mass Spectrometry

Immune monitoring in cancer immunotherapy involves screening CD8+ T-cell responses against neoantigens, the tumor-specific peptides presented by Major histocompatibility complex Class I (MHCI) on the cell surface. High-throughput immune monitoring requires methods to produce and characterize small quantities of thousands of MHCI-peptide complexes that may be tested for a patient's T-cell response. MHCI synthesis has been achieved using a photocleavable peptide that is exchanged by the neoantigen; however, assays that measure peptide exchange currently disassemble the complex prior to analysis_precluding direct molecular characterization. Here, we use native mass spectrometry (MS) to profile intact recombinant MHCI complexes and directly measure peptide exchange. Coupled with size-exclusion chromatography or capillary-zone electrophoresis, the assay identified all tested human leukocyte antigen (HLA)/peptide combinations in the nanomole to picomole range with minimal run time, reconciling the synthetic and analytical requirements of MHCI-peptide screening with the downstream T-cell assays. We further show that the assay can be "multiplexed" by measuring exchange of multiple peptides simultaneously and also enables calculation of Vc(50), a measure of gas-phase stability. Additionally, MHCI complexes were fragmented by top-down sequencing, demonstrating that the intact complex, peptide sequence, and their binding affinity can be determined in a single analysis. This screening tool for MHCI-neoantigen complexes represents a step toward the application of state-of-the-art MS technology in translational settings. Not only is this assay already informing on the viability of immunotherapy in practice, the platform also holds promise to inspire novel MS readouts for increasingly complex biomolecules used in the diagnosis and treatment of disease.

Author Info: (1) Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, California 94080, United States. (2) Department of Microchemistry, Proteomics and

Author Info: (1) Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, California 94080, United States. (2) Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, California 94080, United States. (3) BGI Americas, San Jose, California 95134, United States. (4) Department of Protein Chemistry, Genentech Inc., South San Francisco, California 94080, United States. (5) 908 Devices, Carrboro, North Carolina 27510, United States. (6) 908 Devices, Carrboro, North Carolina 27510, United States. (7) Thermo Fisher Scientific, San Jose, California 95134, United States. (8) Thermo Fisher Scientific, San Jose, California 95134, United States. (9) Department of Protein Chemistry, Genentech Inc., South San Francisco, California 94080, United States. (10) Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, California 94080, United States.

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

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 CAR(High) T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CAR(High) 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.

Author Info: (1) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (2) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Computational

Author Info: (1) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (2) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Computational Biology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (3) Computational Biology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (4) Department of Hematology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (5) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (6) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (7) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (8) Department of Immunology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (9) Hematology Service, Hospital Universitario de Navarra, IdiSNA, Pamplona, Spain. (10) Immunology and Immunotherapy Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (11) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (12) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. (13) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (14) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. (15) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (16) Flow Cytometry Core, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (17) Hematology Service, Hospital Universitario de Navarra, IdiSNA, Pamplona, Spain. (18) Hematology Service, Hospital Universitario de Navarra, IdiSNA, Pamplona, Spain. (19) Department of Immunology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (20) Department of Hematology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (21) Department of Immunology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. Immunotherapy platform Hospital Sant Joan de DŽu, Barcelona, Spain. (22) Department of Hematology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (23) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (24) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. (25) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Flow Cytometry Core, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (26) Immunology and Immunotherapy Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. (27) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Immunology and Immunotherapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (28) Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Immunology and Immunotherapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. (29) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Cancer Center Universidad de Navarra (CCUN), Pamplona, Spain. (30) Department of Hematology, Hospital Clinic de Barcelona, IDIBAPS, Universidad de Barcelona, Barcelona, Spain. (31) Computational Biology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Data Science and Artificial Intelligence Institute (DATAI), Universidad de Navarra, Pamplona, Spain. (32) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. (33) Hemato-Oncology Program, Cima Universidad de Navarra, IdiSNA, Pamplona, Spain. Hematology and Cell Therapy Department, Cl’nica Universidad de Navarra (CUN), Pamplona, Spain. Centro de Investigaci—n BiomŽdica en Red de C‡ncer (CIBERONC), Madrid, Spain. Cancer Center Universidad de Navarra (CCUN), Pamplona, Spain.

Development and characterization of a novel human CD137 agonistic antibody with anti-tumour activity and a good safety profile in non-human primates

CD137 (4-1BB, TNFRSF9), an inducible T cell costimulatory receptor, is expressed on activated T cells, activated NK cells, Treg cells, and several innate immune cells, including DCs, monocytes, neutrophils, mast cells, and eosinophils. In animal models and clinical trials, anti-CD137 agonistic monoclonal antibodies have shown antitumor potential, but balancing the efficacy and toxicity of anti-CD137 agonistic monoclonal antibodies are considerable hindrances for clinical applications. Here, we describe a novel fully human CD137 agonistic antibody (PE0116) generated from immunized Harbour H2L2 human transgenic mice. PE0116 is a ligand blocker, which is also the case for Utomilumab (one of the leading CD137 agonistic drugs); PE0116 partially overlaps with Urelumab's recognized epitope. In vitro, PE0116 activates NF-_B signalling, significantly promotes T cell proliferation, and increases cytokine secretion in the presence of cross-linking. Importantly, PE0116 possesses robust anti-tumor activity in the MC38 tumor model. In vivo, PE0116 exhibits a good safety profile and has typical pharmacokinetic characteristics of an IgG antibody in preclinical studies of non-human primates. In summary, PE0116 is a promising anti-CD137 antibody with a good safety profile in preclinical studies.

Author Info: (1) State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China. Biologics Discovery, Shanghai ChemPartner Co., Ltd, Shanghai, China. (2

Author Info: (1) State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China. Biologics Discovery, Shanghai ChemPartner Co., Ltd, Shanghai, China. (2) Biologics Discovery, Shanghai ChemPartner Co., Ltd, Shanghai, China. (3) Biologics Discovery, Shanghai ChemPartner Co., Ltd, Shanghai, China. (4) Biologics Discovery, Shanghai ChemPartner Co., Ltd, Shanghai, China. (5) Biologics Discovery, Shanghai Hyamab Biotechnology Co., Ltd, Shanghai, China. (6) State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.

A 2-in-1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development

In oncology, dose-finding studies are largely performed only in Phase I clinical trials and the maximum tolerated dose (MTD), a dose initially developed for systemic chemotherapies, is by default selected for the Phase 3 confirmatory trial. With the advent of anti-cancer therapies such as molecular targeted agents and immunotherapies, a paradigm shift is underway from the use of conventional MTD approaches to improved dose selection strategies for oncology programs. In response to this new challenge, new study designs are needed to optimize dose selection while still bring life-changing new therapies to patients as soon as possible. In this paper, we propose a 2-in-1 adaptive design starting with a Phase 2 trial with randomized evaluation of multiple doses and only select one dose to expand to a Phase 3 trial if efficacy evidence is observed based on an interim evaluation. The lowest dose will be selected if multiple doses show promising efficacy unless the higher dose demonstrates a more compelling treatment effect, and study will be seamlessly expanded to a Phase 3 trial with the selected dose with patients enrolled in the Phase 2 portion also used for the statistical inference in the Phase 3 portion. The overall Type I error can be controlled under a mild assumption. Simulation studies are conducted to confirm the control of Type I error and to demonstrate the desirable operating characteristics of the proposed design.

Author Info: (1) BeiGene, Ltd., Ridgefield Park, NJ, USA. Electronic address: sleepingzpy@gmail.com. (2) BeiGene, Ltd., Ridgefield Park, NJ, USA. (3) BeiGene, Ltd., Ridgefield Park, NJ, USA. (4

Author Info: (1) BeiGene, Ltd., Ridgefield Park, NJ, USA. Electronic address: sleepingzpy@gmail.com. (2) BeiGene, Ltd., Ridgefield Park, NJ, USA. (3) BeiGene, Ltd., Ridgefield Park, NJ, USA. (4) BeiGene, Ltd., Ridgefield Park, NJ, USA.

Targeting of palpable B16-F10 melanoma tumors with polyclonal antibodies on white blood cells

BACKGROUND: Antibodies and other recognition molecules direct cancer cell death by multiple types of immune cells. Therapy directed at only one target typically results in tumor regrowth because of tumor heterogeneity. Our goal is to direct therapy to multiple targets simultaneously. Our previous studies showed that multiple antibodies targeting mutated tumor proteins inhibited tumor growth when injected subcutaneously near the time of cancer cell implantation. METHODS: A cocktail of rabbit antibodies against B16-F10 cell surface related mutated proteins were generated. Implanted B16-F10 cells were allowed to grow to palpable size before treatment. Antibodies were administered using different routes of exposure. Free antibody was compared to antibody armed on mouse splenic white blood cells (WBCs). Binding of the antibody cocktail was determined for mouse and human WBCs. RESULTS: The antibody cocktail inhibited tumor growth and prolonged survival when administered as free antibody or armed on WBCs. The antibody cocktail armed on WBCs achieved similar tumor inhibition as free antibody but at a dose 1000-fold less. Armed WBCs achieved tumor inhibition by intravenous and subcutaneous administration. The antibody cocktail bound well to human WBCs and saturation dose was defined. Binding was stable under simulated in vivo condition in human plasma at 37_¡C. CONCLUSIONS: Antibodies targeting multiple tumor mutated proteins inhibited tumor growth and prolonged survival. Effective antibody dose was reduced 1000-fold by arming WBCs. Rabbit antibodies saturated human WBCs using <1_mg per billion cells. A phase I trial in cancer patients using this strategy has been approved by the FDA.

Author Info: (1) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: Girja.Shukla@med.uvm.edu.

Author Info: (1) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: Girja.Shukla@med.uvm.edu. (2) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: Stephanie.Pero@uvm.edu. (3) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: Linda.Mei@uvm.edu. (4) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: Yujing.Sun@med.uvm.edu. (5) Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT, USA. Electronic address: David.Krag@uvm.edu.

Enhancing therapeutic anti-cancer responses by combining immune checkpoint and tyrosine kinase inhibition

Over the past decade, immune checkpoint inhibitor (ICI) therapy has been established as the standard of care for many types of cancer, but the strategies employed have continued to evolve. Recently, much clinical focus has been on combining targeted therapies with ICI for the purpose of manipulating the immune setpoint. The latter concept describes the equilibrium between factors that promote and those that suppress anti-cancer immunity. Besides tumor mutational load and other cancer cell-intrinsic determinants, the immune setpoint is also governed by the cells of the tumor microenvironment and how they are coerced by cancer cells to support the survival and growth of the tumor. These regulatory mechanisms provide therapeutic opportunities to intervene and reduce immune suppression via application of small molecule inhibitors and antibody-based therapies against (receptor) tyrosine kinases and thereby improve the response to ICIs. This article reviews how tyrosine kinase signaling in the tumor microenvironment can promote immune suppression and highlights how therapeutic strategies directed against specific tyrosine kinases can be used to lower the immune setpoint and elicit more effective anti-tumor immunity.

Author Info: (1) Cancer Program, Monash Biomedicine Discovery Institute, Monash University, 23 Innovation Walk, Clayton, VIC, 3800, Australia. Roger.Daly@monash.edu. Department of Biochemistry

Author Info: (1) Cancer Program, Monash Biomedicine Discovery Institute, Monash University, 23 Innovation Walk, Clayton, VIC, 3800, Australia. Roger.Daly@monash.edu. Department of Biochemistry & Molecular Biology, Monash University, 23 Innovation Walk, Clayton, VIC, 3800, Australia. Roger.Daly@monash.edu. (2) Department of Biochemistry & Molecular Biology, Monash University, 23 Innovation Walk, Clayton, VIC, 3800, Australia. Olivia Newton-John Cancer Research Institute and La Trobe University School of Cancer Medicine, 145 Studley Rd, Melbourne-Heidelberg, VIC, 3084, Australia. Department of Molecular Imaging & Therapy, Austin Health, and Faculty of Medicine, University of Melbourne, 145 Studley Rd, Melbourne-Heidelberg, VIC, 3084, Australia. (3) Olivia Newton-John Cancer Research Institute and La Trobe University School of Cancer Medicine, 145 Studley Rd, Melbourne-Heidelberg, VIC, 3084, Australia. (4) Department of Biochemistry & Molecular Biology, Monash University, 23 Innovation Walk, Clayton, VIC, 3800, Australia. Matthias.Ernst@onjcri.org.au. Olivia Newton-John Cancer Research Institute and La Trobe University School of Cancer Medicine, 145 Studley Rd, Melbourne-Heidelberg, VIC, 3084, Australia. Matthias.Ernst@onjcri.org.au.

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