Journal Articles

Case of complete response to immunotherapy in MMR-deficient prostate cancer associated with NK-like and CD4+CD8+ T cells

Spotlight 

In a patient with advanced prostate cancer, pembrolizumab resulted in CR and, followed by a radical prostatectomy, prevented recurrence out to 18mo. Tumor whole-exome NGS indicated dMMR/MSI-H and an extremely high TMB. Among PBMCs, CD56+ NK-like CD8+ T cells and CD4+CD8+ double-positive (DP) T cells were observed at high frequency relative to healthy donors, expressed cytotoxic/effector gene signatures and TEMRA phenotype, and clonally expanded after ICB. In other trials (prostate and dMMR/MSI-H cancers), CD56+CD8+ and DP T cells were found among TILs in ICB-treated patients, and DP T cell expansion was positively associated with patient response.

Contributed by Alex Najibi

In a patient with advanced prostate cancer, pembrolizumab resulted in CR and, followed by a radical prostatectomy, prevented recurrence out to 18mo. Tumor whole-exome NGS indicated dMMR/MSI-H and an extremely high TMB. Among PBMCs, CD56+ NK-like CD8+ T cells and CD4+CD8+ double-positive (DP) T cells were observed at high frequency relative to healthy donors, expressed cytotoxic/effector gene signatures and TEMRA phenotype, and clonally expanded after ICB. In other trials (prostate and dMMR/MSI-H cancers), CD56+CD8+ and DP T cells were found among TILs in ICB-treated patients, and DP T cell expansion was positively associated with patient response.

Contributed by Alex Najibi

ABSTRACT: Mismatch repair deficiency (dMMR) and microsatellite instability (MSI-H) are rare in prostate cancer, occurring in 2%-4% of cases. These defects result in increased genomic instability and elevated tumor mutational burden (TMB), which can support responses to immune checkpoint inhibitors (ICIs). Here, we report a patient with locally advanced Gleason 5 + 5 = 10 prostatic adenocarcinoma harboring MSH2 and MSH6 genomic deletions with ultrahigh TMB (>250 mutations/megabase) in whom pembrolizumab resulted in a striking complete radiographic, pathologic, and molecular response. Using digital-spatial microscopy, single-cell RNA/T cell receptor (TCR) sequencing, and multiplex cytometry, we identify atypical tumor-infiltrating T cells with natural killer-like phenotypes and CD4(+)CD8(+) (double-positive) lymphocytes. These clonal T cell populations expand preferentially following ICI and adopt terminally differentiated and cytotoxic profiles that may drive clinical response. Similar T cells are also present in diverse cancers and expand exclusively in ICI-responsive patients. These findings inform on the cellular mechanisms by which immunotherapies may mediate profound responses in patients with dMMR solid tumors.

Author Info: (1) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesot

Author Info: (1) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA; Center for Immunology, University of Minnesota, Minneapolis, MN 55455, USA. (2) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (3) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (4) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (5) Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA; Clinical Translational Science Institute, University of Minnesota, Minneapolis, MN 55415, USA. (6) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (7) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (8) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. (9) Department of Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA. (10) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (11) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. (12) Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. (13) Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. (14) Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. (15) Caris Life Sciences, Phoenix, AZ 85040, USA. (16) Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA. (17) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (18) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (19) Allina Health Cancer Institute, Minneapolis, MN 55407, USA. (20) Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA. (21) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (22) Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA; Clinical Translational Science Institute, University of Minnesota, Minneapolis, MN 55415, USA. (23) Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. (24) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. (25) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. (26) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. (27) Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address: anton401@umn.edu.

Activation of tumor-specific CD8+ T cells prior to radiopharmaceutical therapy improves antitumor response Spotlight 

Shim et al. investigated how the timing of tumor antigen-specific CD8+ T cell activation shaped responses to radiopharmaceutical therapy with ⁹⁰Y-NM600 (RPT) in E.G7-OVA and TRAMP-C1 tumor models. Low-dose RPT led to an increase in CD8+ T cell infiltration, but failed to enrich antigen-specific CD8+ T cells. Ex vivo- or (with vaccination) in vivo-activated, but not naive, OT-I cells given prior to RPT slowed tumor growth and expanded antigen-specific effector memory CD8+ T cells in a type I IFN-dependent, cGAS–STING-independent manner. In the TRAMP-C1 prostate cancer model, an AR-encoding DNA vaccine given prior to RPT enhanced tumor control.

Contributed by Shishir Pant

Shim et al. investigated how the timing of tumor antigen-specific CD8+ T cell activation shaped responses to radiopharmaceutical therapy with ⁹⁰Y-NM600 (RPT) in E.G7-OVA and TRAMP-C1 tumor models. Low-dose RPT led to an increase in CD8+ T cell infiltration, but failed to enrich antigen-specific CD8+ T cells. Ex vivo- or (with vaccination) in vivo-activated, but not naive, OT-I cells given prior to RPT slowed tumor growth and expanded antigen-specific effector memory CD8+ T cells in a type I IFN-dependent, cGAS–STING-independent manner. In the TRAMP-C1 prostate cancer model, an AR-encoding DNA vaccine given prior to RPT enhanced tumor control.

Contributed by Shishir Pant

BACKGROUND: Radiopharmaceutical therapy (RPT) delivers radiation systemically, enabling the treatment of metastatic cancers. Beyond killing tumor cells, RPT can modulate the tumor immune microenvironment. With RPTs and immunotherapies already approved or in development for prostate cancer, many preclinical and clinical studies are evaluating their use in combination. However, due to the radiosensitivity of tumor-infiltrating lymphocytes, further studies are needed to determine the effects of RPT on these cells to better inform the sequence of immunotherapies that activate T cells when given with RPT. METHODS: E.G7-OVA tumor-bearing mice received na•ve or activated OT-I CD8+T cells prior to or following the administration of RPT using (90)Y-NM600. Changes in tumor growth were monitored, and tumor-infiltrating lymphocytes were evaluated for phenotypic and functional markers. The murine prostate tumor model TRAMP-C1 was used to evaluate this approach using tumor antigen-specific vaccination with (90)Y-NM600. RESULTS: Antitumor efficacy was improved if OT-I CD8+T cells were present and activated prior to (90)Y-NM600 administration than if the cells were delivered after RPT. Similarly, in vivo activation of adoptively transferred OT-I CD8+T cells, using ovalbumin (OVA)-specific vaccination, prior to RPT slowed tumor growth and increased the frequency of tumor-infiltrating OVA(257-264)-specific CD8+T cells with effector memory phenotype and effector molecule production. Blockade of type I interferon, but not the upstream inhibition of stimulator of interferon genes, abrogated tumor growth delay resulting from the combination treatment. Tumor antigen-specific vaccination prior to (90)Y-NM600 administration similarly improved antitumor outcomes in the TRAMP-C1 tumor model. CONCLUSIONS: Our study suggests that tumor-specific CD8+T cells need to be present and activated prior to RPT to enhance antitumor outcomes. This study highlights the importance of considering the effects of RPT on tumor-infiltrating CD8+T cells when combining other T-cell activating therapies with RPT, as they may similarly display sequence-dependent antitumor outcomes.

Author Info: (1) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (2) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (3) University of Wisconsin

Author Info: (1) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (2) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (3) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (4) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (5) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (6) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (7) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (8) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA. (9) University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA dm3@medicine.wisc.edu.

Neutrophil regulation of immunotherapy for cancer is controlled by type II interferon Featured  

Pei et al. found that the IFNγ produced in tumors during treatment with various immunotherapies induced PD-L1 expression by neutrophils and drove them towards an aged/immunosuppressive phenotype, which contributed to treatment resistance. This could be alleviated by eliminating neutrophils or disrupting type II IFN signaling or PD-L1 expression, which shifted neutrophil polarization to a more pro-inflammatory state. The accumulation of aged, PD-L1+ neutrophils was also evident in data from immunotherapy-treated human tumors, suggesting possible avenues for intervention to improve immunotherapy responses.

Pei et al. found that the IFNγ produced in tumors during treatment with various immunotherapies induced PD-L1 expression by neutrophils and drove them towards an aged/immunosuppressive phenotype, which contributed to treatment resistance. This could be alleviated by eliminating neutrophils or disrupting type II IFN signaling or PD-L1 expression, which shifted neutrophil polarization to a more pro-inflammatory state. The accumulation of aged, PD-L1+ neutrophils was also evident in data from immunotherapy-treated human tumors, suggesting possible avenues for intervention to improve immunotherapy responses.

ABSTRACT: Tumor resistance to immunotherapy is driven by several mechanisms, including those imposed by myeloid populations. Neutrophils are prominent within this landscape and display functional heterogeneity. Here, we investigated the contextual role of neutrophils, and using neutropenic mice, we found that the dominating function was to block the response when targeting T cells or myeloid cells. We found that neutrophils upregulated programmed death ligand-1 (PD-L1) in response to the treatment and, using this as a target, depleted this population. The upregulation of PD-L1 was dependent on interferon-γ (IFN-γ) produced by cytotoxic lymphocytes. Specific genetic deletion of cd274 or Ifngr1 on neutrophils showed that this was cell intrinsic. Moreover, in the absence of the capacity for specific IFN-γ-driven suppression, neutrophils changed their phenotype to support immunotherapy. Thus, we find that the type II interferon, IFN-γ, is key in determining whether neutrophils will support or block immunotherapy for cancer.

Author Info: (1) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. (2) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Sto

Author Info: (1) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. (2) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. (3) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. (4) Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden. (5) Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden. (6) Department of Gastroenterology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. (7) Laboratory of Molecular Genetics and Immunology, Rockefeller University, New York, NY, USA. (8) University of Munster, Institute of Experimental Pathology, Munster, Germany. (9) Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. Electronic address: mikael.karlsson@ki.se.

Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma Spotlight 

Using bulk DNA/RNA sequencing and single-nucleus RNAseq, Ghannam et al. profiled 181 ICB-treated glioblastomas, benchmarking against standard-of-care cohorts, to define genomic correlates of ICB response. At baseline, an mesenchymal (MES) transcriptional subtype with high HLA class I expression and increased T cell infiltration was predictive of improved survival after ICB, but not chemoradiation, whereas non-MES-linked lesions were associated with worse ICB outcomes. TMB was not predictive of outcomes, and a longitudinal analysis showed ICB selected for subclones with non-MES features as a trajectory of acquired ICB resistance in GBM.

Contributed by Shishir Pant

Using bulk DNA/RNA sequencing and single-nucleus RNAseq, Ghannam et al. profiled 181 ICB-treated glioblastomas, benchmarking against standard-of-care cohorts, to define genomic correlates of ICB response. At baseline, an mesenchymal (MES) transcriptional subtype with high HLA class I expression and increased T cell infiltration was predictive of improved survival after ICB, but not chemoradiation, whereas non-MES-linked lesions were associated with worse ICB outcomes. TMB was not predictive of outcomes, and a longitudinal analysis showed ICB selected for subclones with non-MES features as a trajectory of acquired ICB resistance in GBM.

Contributed by Shishir Pant

ABSTRACT: The determinants of immune checkpoint blockade (ICB) response in glioblastoma (GBM) with wild-type isocitrate dehydrogenase remain poorly understood. Here we profiled 181 ICB-treated GBM cases using bulk DNA sequencing, bulk RNA sequencing and single-nucleus RNA sequencing to investigate the genomic features associated with ICB outcomes. Baseline tumor transcriptional subtype was predictive of overall survival following ICB, with mesenchymal (MES) GBM associated with improved outcomes to ICB but not standard chemoradiation. Non-MES-associated genetic lesions, including those in PDGFRA and CDKN2A, were associated with worse survival following ICB but not standard therapy. Tumor mutational burden was not predictive of outcomes. Survival was associated with pre-ICB enrichment for MES-like malignant cells, marked by high human leukocyte antigen class I expression and greater T cell infiltration. Paired tumor analyses linked ICB exposure to outgrowth of subclones harboring lesions associated with non-MES subtypes, supporting MES-to-non-MES transition as a common trajectory of acquired resistance to ICB, distinct from standard chemoradiation.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Harvard/MIT MD-PhD Program and Harvard Immunology PhD Program, Harvard Medical School, Boston, MA, USA. (2) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Molecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (3) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (4) Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. (5) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (6) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (7) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (8) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (9) Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada. (10) Harvard Medical School, Boston, MA, USA. Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (11) Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, USA. (12) Department of Imaging, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA. (13) Departments of Radiology, Mass General Brigham, Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA. (14) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (15) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (16) Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (17) Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (18) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (19) Departments of Radiology, Mass General Brigham, Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA. (20) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (21) IBM Research, Yorktown Heights, NY, USA. (22) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (23) Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (24) Center for Tumors of the Nervous System, Mass General Brigham Cancer Institute & Department of Neurosurgery, Mass General Brigham, Boston, MA, USA. (25) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (26) Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA. (27) Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. (28) Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (29) Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. (30) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Harvard Medical School, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. catherine_wu@dfci.harvard.edu.

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer Spotlight 

Azimazade et al. developed an explainable machine learning (XML) pipeline to study associations between clinical outcomes and in silico estimated cell types within the TIME of over 5,000 METABRIC and TCGA samples from patients with breast cancer. In estrogen receptor-positive samples, macrophages correlated positively with pathological complete responses after neoadjuvant chemotherapy, but negatively with relapse-free survival. Imaging mass cytometry and scRNAseq data demonstrated that HLA-ABC+ macrophages accumulated in the vicinity of HLA-ABChi epithelial cells and were associated with Tregs and TEX cells.

Contributed by Ute Burkhardt

Azimazade et al. developed an explainable machine learning (XML) pipeline to study associations between clinical outcomes and in silico estimated cell types within the TIME of over 5,000 METABRIC and TCGA samples from patients with breast cancer. In estrogen receptor-positive samples, macrophages correlated positively with pathological complete responses after neoadjuvant chemotherapy, but negatively with relapse-free survival. Imaging mass cytometry and scRNAseq data demonstrated that HLA-ABC+ macrophages accumulated in the vicinity of HLA-ABChi epithelial cells and were associated with Tregs and TEX cells.

Contributed by Ute Burkhardt

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis remain poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devise an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying this pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we create a comprehensive catalog of the TME's role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) data analysis, we find that macrophages tend to accumulate in the vicinity of HLA-ABC(hi) epithelial cells as their frequency increases in tumor tissues and that they also express elevated levels of HLA-ABC protein. In both IMC and single-cell RNA sequencing (scRNA-seq) data, we uncover a significant association between these HLA-ABC(hi) macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicts interactions between HLA-ABC(hi) macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, this work enhances the characterization of macrophage-associated immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

Author Info: (1) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. younessazimzade@gmail.com. (2) Department of Tumor Biology, Institute for Cancer Research, Div

Author Info: (1) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. younessazimzade@gmail.com. (2) Department of Tumor Biology, Institute for Cancer Research, Division of Cancer Medicine, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway. (3) Department of Medical Genetics, Oslo University Hospital, University of Oslo, Oslo, Norway. (4) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. arnoldo.frigessi@medisin.uio.no. Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway. arnoldo.frigessi@medisin.uio.no. (5) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. a.k.luque@medisin.uio.no. Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway. a.k.luque@medisin.uio.no.

Targeting CCR1 remodels the tumor microenvironment and relieves immune suppression in pancreatic cancer Featured  

Evaluating the role of CCR1 in pancreatic cancer, Zhang et al. used KC and KPC mouse tumor models, and found while elimination of CCR1 did not limit tumor formation, it delayed progression of active disease, resulting in prolonged survival. CCR1 was mainly expressed by macrophages and granulocytes, but its deletion induced TIME remodeling that affected fibroblasts and increased CD8+ T cell accumulation, but not activation. CCR1 inhibition showed synergy in combination with targeting of other immunosuppressive mechanisms, though there was still room to improve antitumor efficacy in this highly resistant tumor setting.

Evaluating the role of CCR1 in pancreatic cancer, Zhang et al. used KC and KPC mouse tumor models, and found while elimination of CCR1 did not limit tumor formation, it delayed progression of active disease, resulting in prolonged survival. CCR1 was mainly expressed by macrophages and granulocytes, but its deletion induced TIME remodeling that affected fibroblasts and increased CD8+ T cell accumulation, but not activation. CCR1 inhibition showed synergy in combination with targeting of other immunosuppressive mechanisms, though there was still room to improve antitumor efficacy in this highly resistant tumor setting.

ABSTRACT: A hallmark of pancreatic cancer is an extensive fibroinflammatory stroma. Myeloid cells, including abundant macrophages, are a prevalent cellular component of the pancreatic cancer microenvironment and a key driver of immunosuppression. Identifying mechanisms of myeloid-cell driven immunosuppression is thus key to developing therapeutic approaches. Harnessing single-cell RNA sequencing data from human and murine tumors, we determined that tumor infiltrating myeloid cells (including macrophages and granulocytes) have elevated expression of C-C motif chemokine receptor 1 (CCR1). To determine the functional role of CCR1, we generated oncogenic KRAS based genetically engineered mouse models of pancreatic cancer, with or without addition of a mutant form of the tumor suppressor Trp53 (KC and KPC, respectively), lacking CCR1 expression. CCR1 inactivation did not affect formation of early lesions, but delayed progression to cancer and resulted in prolonged survival. In these mice, macrophages lacking CCR1 had reduced expression of the immunosuppressive marker Arginase 1. Loss of CCR1 also profoundly shifted the prevalent fibroblast population, inducing a pancreatic stellate cell-like phenotype. In two independent syngeneic orthotopic models, ablation or pharmacologic inhibition of CCR1 reduced tumor growth and increased CD8+ T cell cytotoxic activity, sensitizing tumors to immunotherapy. Our data show that CCR1-expressing myeloid cells promote pancreatic cancer growth through modulation of the immune microenvironment and fibroblasts, indicating that CCR1 might be a suitable target for combination therapy.

Author Info: (1) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (2) University of Michigan-Ann Arbor Ann Arbor, MI United States. (3) University of

Author Info: (1) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (2) University of Michigan-Ann Arbor Ann Arbor, MI United States. (3) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (4) University of Michigan Medical Schooligan United States. (5) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (6) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (7) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (8) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (9) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (10) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (11) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (12) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (13) University of Maryland, Baltimore Baltimore United States. ROR: https://ror.org/04rq5mt64 (14) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (15) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (16) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (17) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (18) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (19) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (20) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (21) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (22) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (23) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (24) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (25) Cornell University Ithaca United States. ROR: https://ror.org/05bnh6r87 (26) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (27) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (28) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (29) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (30) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (31) Cedars-Sinai Medical Center Los Angeles, CA United States. ROR: https://ror.org/02pammg90 (32) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (33) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291

Targeting tumor-intrinsic STK40 induces immune vulnerability and drives T cell reinvigoration Spotlight 

Using in vivo CRISPR screens, Zhu et al. identified serine/threonine kinase 40 (STK40) as a novel regulator of immune evasion in hepatocellular carcinoma (HCC). Stk40 loss disrupted COP1-mediated IFNGR1 degradation, stabilized IFNGR1, restored tumor-intrinsic IFNγ signaling, and sensitized HCC cells to CD8+ T cell-mediated killing. Stk40 deficiency simultaneously induced tumor-derived GM-CSF, enhancing cDC1 infiltration, antigen cross-presentation, and CD8+ T cell activation. LNP-siRNA-mediated STK40 targeting synergized with PD-1 blockade in suppressing tumor growth in multiple cancer models.

Contributed by Shishir Pant

Using in vivo CRISPR screens, Zhu et al. identified serine/threonine kinase 40 (STK40) as a novel regulator of immune evasion in hepatocellular carcinoma (HCC). Stk40 loss disrupted COP1-mediated IFNGR1 degradation, stabilized IFNGR1, restored tumor-intrinsic IFNγ signaling, and sensitized HCC cells to CD8+ T cell-mediated killing. Stk40 deficiency simultaneously induced tumor-derived GM-CSF, enhancing cDC1 infiltration, antigen cross-presentation, and CD8+ T cell activation. LNP-siRNA-mediated STK40 targeting synergized with PD-1 blockade in suppressing tumor growth in multiple cancer models.

Contributed by Shishir Pant

ABSTRACT: Immunotherapy has revolutionized cancer treatment, yet its efficacy in hepatocellular carcinoma (HCC) remains limited and the mechanisms of resistance are poorly defined. Using in vivo CRISPR-Cas9 screens, we identify serine/threonine kinase 40 (STK40) as a previously unrecognized regulator of immune evasion. Stk40 ablation synergizes with PD-1 blockade to induce tumor regression. Hepatocyte-specific Stk40 deletion abolishes tumorigenesis in hydrodynamic plasmid-driven HCC models. Mechanistically, STK40 scaffolds the COP1 ubiquitin ligase to promote interferon gamma receptor 1 (IFNGR1) degradation. Genetic depletion of Stk40 stabilizes IFNGR1, restoring tumor cell sensitivity to T cell cytotoxicity. Concurrently, Stk40 loss triggers autonomous GM-CSF secretion, enhancing the infiltration and activation of conventional type 1 dendritic cells, which promotes antigen cross-presentation and CD8(+) T cell activation. Pharmacological inhibition of STK40 using LNP-siRNA, combined with PD-1 blockade, elicits potent anti-tumor responses across multiple cancer types. These findings establish STK40 as a dual-action therapeutic target to overcome resistance to anti-tumor immunity.

Author Info: (1) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine,

Author Info: (1) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (2) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (3) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Immune Therapy Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (4) Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (5) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (6) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (7) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (8) Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China. (9) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (10) Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (11) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (12) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (13) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (14) The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China. (15) Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China. (16) German Cancer Research Center, Division Immune Regulation in Cancer, Heidelberg, Germany. (17) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands. Electronic address: r.bernards@nki.nl. (18) Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: yuyang@shsmu.edu.cn. (19) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: wxqin@sjtu.edu.cn. (20) State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: cwang@shsci.org.

Immune-remodeling mRNAs expressing IRF8 or NIK generate durable antitumor immunity in multiple cancer models Spotlight 

In mice, i.t. or i.v. delivery of CKK-E12-LNPs loaded with immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase (NIK) or IFN regulatory factor 8 (IRF8) induced (1) APC activation and maturation into cDC1s, (2) a release of immunostimulatory cytokines, (3) accumulation of NKT and γδT cells in tumors, and (4) priming of antitumor CD8+ T cells, which infiltrated and eliminated tumors and protected mice from rechallenge. In combination with mRNA encoding OVA, IR-mRNA prevented growth of OVA+ tumors. IR-mRNAs also synergized with anti-PD-1, and enhanced humoral and adaptive immune responses to infectious disease antigens.

Contributed by Lauren Hitchings

In mice, i.t. or i.v. delivery of CKK-E12-LNPs loaded with immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase (NIK) or IFN regulatory factor 8 (IRF8) induced (1) APC activation and maturation into cDC1s, (2) a release of immunostimulatory cytokines, (3) accumulation of NKT and γδT cells in tumors, and (4) priming of antitumor CD8+ T cells, which infiltrated and eliminated tumors and protected mice from rechallenge. In combination with mRNA encoding OVA, IR-mRNA prevented growth of OVA+ tumors. IR-mRNAs also synergized with anti-PD-1, and enhanced humoral and adaptive immune responses to infectious disease antigens.

Contributed by Lauren Hitchings

ABSTRACT: Although immunotherapy has benefited a subset of persons with cancer, its broader efficacy remains limited, primarily because of an immunosuppressive tumor microenvironment characterized by insufficient numbers of functional tumor-specific T cells, antigen-presenting cells (APCs) and tumor-infiltrating lymphocytes. Here we engineer immune cells in the tumor microenvironment using lipid nanoparticles (LNPs) to deliver immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase or interferon regulatory factor 8. These IR-mRNAs activate APCs in tumors, significantly increasing activated type 1 conventional dendritic cells, immunostimulatory cytokines and priming antitumor CD8+ T cells. IR-mRNAs encapsulated in LNPs elicited durable antitumor responses in multiple syngeneic mouse tumor models through both intratumoral and intravenous delivery. Coadministration of IR-mRNA and ovalbumin mRNA elicited a ~10-fold increase in antigen-specific CD8+ T cell responses, sustained long-term memory and effectively prevented tumor growth in vaccinated mice. Additionally, coadministration of IR-mRNA and hemagglutinin mRNA enhanced the humoral response ~5-fold and the cellular response ~15-fold, underscoring their potential as adjuvants for boosting adaptive immunity.

Author Info: (1) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute

Author Info: (1) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (2) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. (3) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (4) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (5) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (6) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA. (7) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. (8) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (9) Bioinformatics & Computing Core Facility of the Swanson Biotechnology Center, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. (10) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (11) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. Department of Systems Biology, Harvard Medical School, Boston, MA, USA. Department of Radiology, Massachusetts General Brigham, Boston, MA, USA. (12) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. cgarris@mgh.harvard.edu. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. cgarris@mgh.harvard.edu. (13) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.

Differential assembly of mouse and human tumor microenvironments Spotlight 

Courau et al. profiled immune landscapes of 15 common mouse tumor models alongside human datasets. Most murine TIMEs resembled a minority subset of macrophage-rich, poorly infiltrated human tumors. Cross-species analysis showed species-specific biases in chemokine networks (including reduced CCR2/CCR5 and altered CXCL13 in mice) and altered T and myeloid cell frequencies, while conserved cell-type specific gene expression programs emerged as discriminatory. An IFN-responsive myeloid–CD8+ T cell cytotoxicity module was conserved across tumor types, and predicted clinical outcome in humans.

Contributed by Shishir Pant

Courau et al. profiled immune landscapes of 15 common mouse tumor models alongside human datasets. Most murine TIMEs resembled a minority subset of macrophage-rich, poorly infiltrated human tumors. Cross-species analysis showed species-specific biases in chemokine networks (including reduced CCR2/CCR5 and altered CXCL13 in mice) and altered T and myeloid cell frequencies, while conserved cell-type specific gene expression programs emerged as discriminatory. An IFN-responsive myeloid–CD8+ T cell cytotoxicity module was conserved across tumor types, and predicted clinical outcome in humans.

Contributed by Shishir Pant

ABSTRACT: Mouse models are frequently used to develop treatments for human cancer. However, the degree to which their tumor microenvironments (TMEs) are synonymously assembled is particularly poorly characterized. Through systematic immunoprofiling of 15 commonly used mouse models, we found that most murine TMEs recapitulate the composition of poorly infiltrated human tumors, extensively biased toward high macrophage densities. We discovered substantial species-specific biases of chemokine expression networks known to drive TMEs assembly, together with discoordinated frequencies of T and myeloid cell subtypes. Even with variable alignment, conserved cell-type-specific gene expression programs emerged across species and cohorts. Dissecting the coordinated T cell-myeloid gene expression programs revealed a conserved axis between interferon-responsive myeloid states and ongoing T cell cytotoxicity that transcends tissue of origin and predicts clinical outcome. Collectively, this work provides a practical atlas outlining both the hazards and opportunities of using mice to model human cancer.

Author Info: (1) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. tristan.courau@u

Author Info: (1) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. CoLabs, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. (2) CoLabs, UCSF, San Francisco, CA, USA. (3) ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (4) CoLabs, UCSF, San Francisco, CA, USA. (5) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (6) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (7) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (8) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (9) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (10) CoLabs, UCSF, San Francisco, CA, USA. (11) CoLabs, UCSF, San Francisco, CA, USA. (12) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (13) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (14) CoLabs, UCSF, San Francisco, CA, USA. (15) CoLabs, UCSF, San Francisco, CA, USA. (16) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (17) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (18) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (19) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (20) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (21) The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. (22) CoLabs, UCSF, San Francisco, CA, USA. Department of Medicine, Division of Rheumatology, UCSF, San Francisco, CA, USA. (23) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. Department of Medicine, Division of Gastroenterology, UCSF, San Francisco, CA, USA. (24) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. max.krummel@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. max.krummel@ucsf.edu.

Loss of the autoimmune risk gene TREX1 reveals a convergence of mechanisms promoting immune tolerance loss and antitumor immunity Spotlight 

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

ABSTRACT: Checkpoint inhibitors targeting PD-1 and CTLA-4 have transformed cancer therapy. Both are genetically associated with autoimmune disorders. Moreover, certain immune-related adverse events and autoimmune risk variants are linked to the clinical efficacy of checkpoint inhibition. These associations suggest common principles governing successful cancer immunotherapy and autoimmune susceptibility. Here, we show that ablation of the cytosolic DNA exonuclease TREX1 predisposes mice to autoimmunity while promoting robust antitumor immunity. Constitutive TREX1 loss leads to early onset autoimmunity, characterized by multiorgan CD8+ T cell infiltration, myocarditis, and Sjgren's syndrome-like disease. In contrast, induced systemic TREX1 ablation is well tolerated and promotes effective CD8+ T cell-driven antitumor immunity. Detailed phenotypic studies revealed a notable overlap between productive antitumor and pathogenic autoimmune CD8+ T cell responses. Collectively, we provide mechanistic evidence for interrelated mechanisms underlying autoimmunity and successful cancer immunotherapy, uncover key parallels between adaptive T cell and innate immune checkpoints, and suggest that targeting autoimmune risk genes represents a promising future avenue for cancer immunotherapy.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

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