Journal Articles

ILT2 identifies an unexploited pool of intratumoral CD8+ bystander T cells with TCR-independent cytotoxicity in renal cell carcinoma Spotlight 

Analyzing primary ccRCC samples, Laboureur et al. identified a subset of CD8+ILT2+PD-1- TILs that were terminally differentiated and expressed high baseline levels of granzyme B, perforin, and NKG2D, but lacked tumor specificity and tissue residency markers. These “bystander” T cells, likely recruited from the periphery, exhibited CD3-driven activation and IFNγ production in response to viral antigens, as well as IL-15/NKG2D-driven, TCR-independent cytotoxicity. The innate-like cytotoxicity was inhibited by HLA-G (an ILT2 ligand expressed on tumor cells), suggestive of a checkpoint axis that could potentially be targeted for immunotherapy.

Contributed by Lauren Hitchings

Analyzing primary ccRCC samples, Laboureur et al. identified a subset of CD8+ILT2+PD-1- TILs that were terminally differentiated and expressed high baseline levels of granzyme B, perforin, and NKG2D, but lacked tumor specificity and tissue residency markers. These “bystander” T cells, likely recruited from the periphery, exhibited CD3-driven activation and IFNγ production in response to viral antigens, as well as IL-15/NKG2D-driven, TCR-independent cytotoxicity. The innate-like cytotoxicity was inhibited by HLA-G (an ILT2 ligand expressed on tumor cells), suggestive of a checkpoint axis that could potentially be targeted for immunotherapy.

Contributed by Lauren Hitchings

ABSTRACT: Immune checkpoint inhibitors (ICIs) have improved clear-cell renal cell carcinoma (ccRCC) therapy, yet many patients remain unresponsive. Alternative strategies are needed, and the HLA-G/ILT2 axis has emerged as a promising immunosuppressive pathway. Here, we deeply characterized CD8_ILT2_ tumor-infiltrating lymphocytes (TILs) as a distinct subset from CD8_PD1_ TILs in ccRCC, using high-dimensional spectral flow cytometry, single-cell transcriptomics, and TCR clonotype analysis. CD8_ILT2_ TILs were terminally differentiated, highly cytotoxic "bystander" cells, enriched for virus-specific TCRs. They phenotypically, transcriptionally and functionally mirrored their circulating counterparts, suggesting peripheral recruitment. In dynamic co-culture assays, they exhibited potent TCR-independent cytotoxicity, mediated by activating innate receptors, namely NKG2D. However, HLA-G inhibited this activity, underscoring the immune-evasive role of the HLA-G/ILT2 axis. Our study defines CD8_ILT2_ TILs as an untapped effector population with potential antitumor activity and a promising therapeutic target in ccRCC. These findings offer new insights into TIL functional diversity and pave the way for innovative immunotherapies beyond conventional ICIs.

Author Info: (1) CEA Fontenay-aux-Roses Paris France. ROR: https://ror.org/010j2gw05 (2) H™pital Saint-Louis Paris France. ROR: https://ror.org/049am9t04 (3) CEA Grenoble Grenoble France. ROR:

Author Info: (1) CEA Fontenay-aux-Roses Paris France. ROR: https://ror.org/010j2gw05 (2) H™pital Saint-Louis Paris France. ROR: https://ror.org/049am9t04 (3) CEA Grenoble Grenoble France. ROR: https://ror.org/02mg6n827 (4) CEA Fontenay-aux-Roses Paris France. ROR: https://ror.org/010j2gw05 (5) Inserm Paris France. ROR: https://ror.org/02vjkv261 (6) Inserm Paris France. ROR: https://ror.org/02vjkv261 (7) CEA Fontenay-aux-Roses Paris France. ROR: https://ror.org/010j2gw05 (8) APHP Paris France. (9) H™pital Saint-Louis, Assistance publique H™pitaux de Paris Paris France. (10) APHP Paris France. (11) Assistance Publique - H™pitaux de Paris Paris France. ROR: https://ror.org/00pg5jh14 (12) CEA Paris-Saclay - Etablissement de Fontenay-aux-roses Paris France. (13) Commissariat ˆ l'Energie Atomique et aux Energies Alternatives Paris France. (14) AP-HP, H™pital Saint-Louis Paris France. (15) Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA) Grenoble France. (16) Inserm Paris France. ROR: https://ror.org/02vjkv261 (17) CEA Fontenay-aux-Roses Paris France. ROR: https://ror.org/010j2gw05 (18) Atomic Energy and Alternative Energies Commission Paris France. ROR: https://ror.org/00jjx8s55

Tumor-resident T cells and dendritic cells form an in situ archetype during immunotherapy response in melanoma Spotlight 

Pietro and Au et al. profiled melanoma lymph node metastases from untreated, ICB-resistant, and ICB-responsive patients using flow cytometry, mIHC, and single-cell transcriptomics to dissect tumor-resident (TR) T cell niches. ICB-responsive tumors were enriched for clonally expanded, cytotoxic CD8⁺ TR cells and cytotoxic/helper CD4⁺ TR cells within an immune-activated microenvironment, whereas ICB-resistant tumors displayed chronic IFNγ signaling, exhausted T cell states, and impaired clonal diversification. Spatial analyses identified CD8⁺ TRs, CD4⁺ TRs, and DC3s forming in situ immune triads as an essential feature of ICB responders.

Contributed by Shishir Pant

Pietro and Au et al. profiled melanoma lymph node metastases from untreated, ICB-resistant, and ICB-responsive patients using flow cytometry, mIHC, and single-cell transcriptomics to dissect tumor-resident (TR) T cell niches. ICB-responsive tumors were enriched for clonally expanded, cytotoxic CD8⁺ TR cells and cytotoxic/helper CD4⁺ TR cells within an immune-activated microenvironment, whereas ICB-resistant tumors displayed chronic IFNγ signaling, exhausted T cell states, and impaired clonal diversification. Spatial analyses identified CD8⁺ TRs, CD4⁺ TRs, and DC3s forming in situ immune triads as an essential feature of ICB responders.

Contributed by Shishir Pant

ABSTRACT: Tumor-resident (TR) T cells, known as tissue-resident memory (TRM) T cells in mice, play a central role in melanoma immunosurveillance, yet their contribution to immune checkpoint inhibitor (ICI) therapy has not been comprehensively explored. We performed spatial and single-cell profiling on 32 metastatic melanoma lymph node samples, from treatment-naïve, ICI-resistant and ICI-responsive patients. Here we show that tumor areas in ICI-responders were enriched for both CD8+ and CD4+ TR. CD8+ TR cells were clonally expanded, and both CD8+ and CD4+ TR cells upregulated cytotoxicity-related gene expression, suggesting functional anti-tumor immunity. Conversely, ICI-resistant tumors displayed chronic IFN-γ response pathways, linked to T cell exhaustion. We further identified a spatially organized immune triad composed of CD8⁺ TR, CD4⁺ TR, and type-3 dendritic cells (DC3) that is exclusive to responding tumors. These findings define coordinated cellular interactions within the tumor microenvironment that underpin successful immunotherapy and provide a framework for spatial biomarkers of response.

Author Info: (1) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Austra

Author Info: (1) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (2) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (3) Bioinformatics, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (4) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Bioinformatics, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (5) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (6) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (7) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (8) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (9) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (10) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (11) Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (12) Roche Innovation Center, Zurich, Switzerland. (13) Roche Innovation Center Basel, Roche Pharma Research and Early Development, Basel, Switzerland. (14) Roche Innovation Center Basel, Roche Pharma Research and Early Development, Basel, Switzerland. (15) Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. (16) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Melanoma Research Victoria, Melbourne, VIC, Australia. Division of Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (17) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Melanoma Research Victoria, Melbourne, VIC, Australia. Division of Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (18) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Melanoma Research Victoria, Melbourne, VIC, Australia. Division of Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (19) Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (20) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (21) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (22) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (23) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (24) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (25) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. Melanoma Research Victoria, Melbourne, VIC, Australia. Division of Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. Cancer Biology and Therapeutics Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. (26) Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia. (27) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. paul.neeson@petermac.org. Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. paul.neeson@petermac.org.

Cuproptosis-immunity crosstalk informs strategy to overcome immunotherapy resistance Spotlight 

Lei, Lu, and Xu et al. showed that cuproptosis induced immunogenic cell death, releasing DAMPs that drove DC maturation, DC-dependent cross-priming, and M1-like TAM and effector CD8+ T cell remodeling, with enhanced tumor suppression in immunocompetent hosts. CD8+ T cell-derived IFNγ activated STAT1–IRF1 signaling to upregulate mitochondrial FDX1 in tumor cells, increasing protein lipoylation and sensitization to cuproptosis. In breast, lung, and pancreatic tumor models, combining cuproptosis inducers with anti-PD-L1 amplified tumoral cuproptosis, increased intratumoral CD8+ T cell functions, and overcame intrinsic and acquired ICB resistance.

Contributed by Shishir Pant

Lei, Lu, and Xu et al. showed that cuproptosis induced immunogenic cell death, releasing DAMPs that drove DC maturation, DC-dependent cross-priming, and M1-like TAM and effector CD8+ T cell remodeling, with enhanced tumor suppression in immunocompetent hosts. CD8+ T cell-derived IFNγ activated STAT1–IRF1 signaling to upregulate mitochondrial FDX1 in tumor cells, increasing protein lipoylation and sensitization to cuproptosis. In breast, lung, and pancreatic tumor models, combining cuproptosis inducers with anti-PD-L1 amplified tumoral cuproptosis, increased intratumoral CD8+ T cell functions, and overcame intrinsic and acquired ICB resistance.

Contributed by Shishir Pant

ABSTRACT: Cuproptosis is a recently identified form of copper-dependent cell death that depends on ferredoxin 1 (FDX1)-mediated protein lipoylation. Here, we reveal that CD8(+) T cell-mediated antitumor immunity enhances tumor cell susceptibility to cuproptosis, leading to a more potent tumor-suppressive effect of cuproptosis inducers in immunocompetent hosts compared with immunodeficient ones. Mechanistically, cuproptotic tumor cells act as a form of immunogenic cell death, releasing damage-associated molecular patterns that activate dendritic cells and enhance antitumor immunity. Reciprocally, CD8(+) T cell-derived interferon (IFN)-_ enhances FDX1 transcription in tumor cells by activating the signal transducer and activator of transcription 1 (STAT1)-IFN regulatory factor-1 (IRF1) signaling axis, resulting in heightened tumor cell sensitivity to cuproptosis. Consequently, combining a cuproptosis inducer with anti-programmed cell death ligand 1 (PD-L1) therapy amplifies tumoral cuproptosis and demonstrates efficacy in overcoming PD-L1 therapy resistance across multiple preclinical models. Our findings unveil a previously unrecognized connection between antitumor immunity and cuproptosis and highlight a potential therapeutic approach to counteract tumor immunotherapy resistance by targeting this unique cell death pathway.

Author Info: (1) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: guanglei_csu@163.com. (2) Departme

Author Info: (1) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: guanglei_csu@163.com. (2) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (3) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (4) Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (5) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (6) Department of Molecular and Cellular Oncology, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (7) Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (8) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (9) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (10) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (11) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (12) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (13) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (14) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (15) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (16) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (17) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (18) Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (19) Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (20) Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (21) Department of Molecular and Cellular Oncology, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (22) Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (23) Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (24) Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (25) Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (26) Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (27) Department of Thoracic and Cardiovascular Surgery, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (28) Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (29) Department of Molecular and Cellular Oncology, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (30) Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (31) Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA. (32) Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (33) Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (34) Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY 10461, USA. (35) Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (36) Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA. Electronic address: bgan@mdanderson.org.

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.

Dissecting the cellular architecture of breast cancer brain metastases reveals prognostically distinct immune landscapes Spotlight 

Jassowicz, Feng, and Warta et al. used tissue cytometry, bulk and single-nucleus RNAseq, flow cytometry, and spatial transcriptomics to profile 156 human breast cancer (BC) brain metastases (BCBM) and map functionally and clinically distinct immune landscapes. Enrichment of intratumoral CD8+ TRM-like cells expressing high activation and effector markers, and the presence of tertiary lymphoid structures (TLSs) in BCBM were identified as two favorable immune landscapes associated with improved prognosis. TRM and TLS gene signatures predicted both overall survival in independent BCBM and primary BC cohorts, and response to ICB across cohorts with various cancer types.

Contributed by Shishir Pant

Jassowicz, Feng, and Warta et al. used tissue cytometry, bulk and single-nucleus RNAseq, flow cytometry, and spatial transcriptomics to profile 156 human breast cancer (BC) brain metastases (BCBM) and map functionally and clinically distinct immune landscapes. Enrichment of intratumoral CD8+ TRM-like cells expressing high activation and effector markers, and the presence of tertiary lymphoid structures (TLSs) in BCBM were identified as two favorable immune landscapes associated with improved prognosis. TRM and TLS gene signatures predicted both overall survival in independent BCBM and primary BC cohorts, and response to ICB across cohorts with various cancer types.

Contributed by Shishir Pant

ABSTRACT: Breast cancer brain metastases (BCBM) are a severe condition with high demand for improved personalized treatment, but a comprehensive understanding of BCBM immune-microenvironment heterogeneity and susceptibility to immunotherapy is lacking. Here, we multimodally profile the immune niche in a clinically well-annotated cohort of 156 BCBM applying tissue cytometry, bulk and single nuclei RNA-sequencing, flow cytometry, and spatial transcriptomics, complemented by functional studies in patient-derived models. Integrative analyses reveal two immune landscapes predicting prolonged patient survival and that are not deducible from paired primary tumors: 1) BCBM with a high proportion of CD8(+) tissue-resident-like memory T cells as major players of tumor immune control. 2) BCBM containing tertiary lymphoid structures. Surrogate signatures of these landscapes are prognostic in independent BCBM and primary breast cancer cohorts, are associated with fewer metastases, and predict immunotherapy response. Our work provides critical insights into anti-tumor immunity in BCBM and identifies novel biomarkers with translational relevance.

Author Info: (1) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Divis

Author Info: (1) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: lena.jassowicz@med.uni-heidelberg.de. (2) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (3) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (4) Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. (5) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (6) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany. (7) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (8) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (9) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (10) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (11) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (12) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (13) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (14) Division Immune Regulation in Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. (15) Division of Molecular and Translational Radiation Oncology and Clinical Cooperation Unit Translational Radiation Oncology, German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg, Germany. (16) Division of Molecular and Translational Radiation Oncology and Clinical Cooperation Unit Translational Radiation Oncology, German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg, Germany. (17) Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany. (18) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (19) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (20) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (21) Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany. (22) Hamamatsu Tissue Imaging and Analysis Center, BioQuant, University of Heidelberg, Heidelberg, Germany. (23) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (24) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (25) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (26) Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway. (27) Single Cell Open Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany. (28) Single Cell Open Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany. (29) National Center for Tumor Diseases (NCT), NCT Heidelberg, a Partnership Between the German Cancer Research Center (DKFZ), the University Hospital Heidelberg (UKHD), The Heidelberg Medical Faculty of the Heidelberg University, and the Thorax Clinic Heidelberg, Heidelberg, Germany. (30) Department of Medical Oncology, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (31) National Center for Tumor Diseases (NCT), NCT Heidelberg, a Partnership Between the German Cancer Research Center (DKFZ), the University Hospital Heidelberg (UKHD), The Heidelberg Medical Faculty of the Heidelberg University, and the Thorax Clinic Heidelberg, Heidelberg, Germany. (32) National Center for Tumor Diseases (NCT), NCT Heidelberg, a Partnership Between the German Cancer Research Center (DKFZ), the University Hospital Heidelberg (UKHD), The Heidelberg Medical Faculty of the Heidelberg University, and the Thorax Clinic Heidelberg, Heidelberg, Germany; Department of Medical Oncology, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Gynecology and Obstetrics, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (33) Division Immune Regulation in Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. (34) Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany. (35) Division of Molecular and Translational Radiation Oncology and Clinical Cooperation Unit Translational Radiation Oncology, German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg, Germany. (36) Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (37) Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (38) Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. (39) Single Cell Open Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany. (40) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), NCT Heidelberg, a Partnership Between the German Cancer Research Center (DKFZ), the University Hospital Heidelberg (UKHD), The Heidelberg Medical Faculty of the Heidelberg University, and the Thorax Clinic Heidelberg, Heidelberg, Germany. (41) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (42) Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: m.seiffert@dkfz-heidelberg.de. (43) Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. Electronic address: h.mende@med.uni-heidelberg.de.

Cytotoxic CD39+ tumor-associated NK cells respond to NKG2A blockade in lung cancer

Featured  

Serger et al. profiled NK cells in NSCLC, and identified two tumor-associated NK cell (taNK; CD103+CD49a+) populations. These cells were cytotoxic, but showed hallmarks of dysfunction. Trajectory analysis showed a transition from the CD56bright phenotype through an interferon response and GZMB induction, leading to the expression of genes related to tissue residency, dysfunction, and cytotoxicity. The CD39-expressing subset of taNK cells had the highest tumor killing capacity and responded to anti-NKG2A therapy.

Serger et al. profiled NK cells in NSCLC, and identified two tumor-associated NK cell (taNK; CD103+CD49a+) populations. These cells were cytotoxic, but showed hallmarks of dysfunction. Trajectory analysis showed a transition from the CD56bright phenotype through an interferon response and GZMB induction, leading to the expression of genes related to tissue residency, dysfunction, and cytotoxicity. The CD39-expressing subset of taNK cells had the highest tumor killing capacity and responded to anti-NKG2A therapy.

ABSTRACT: Natural killer (NK) cell-targeting immunotherapies are emerging, yet the differentiation and functional states of tumor-infiltrating NK cells remain poorly understood. Using matched single-nucleus RNA and ATAC sequencing of samples from patients with non-small cell lung cancer (NSCLC), we resolved the transcriptional and epigenetic landscape of intratumoral NK cells. We identified two tumor-associated NK (taNK) cell subsets marked by expression of ITGAE (CD103) and ITGA1 (CD49a) that display features of tissue residency and dysfunction while preserving cytotoxic function. Trajectory and regulon analyses revealed an inflammation-driven transition from early granzyme K (GZMK)(+) NK cells toward an ENTPD1(+) (CD39(+)) effector state characterized by interferon-stimulated gene (ISG) programs. Functional profiling established CD39(+) taNK cells as the dominant cytotoxic NK cell population with superior killing capacity that was further potentiated by NKG2A blockade. This study offers mechanistic insights into NK cell differentiation in NSCLC and establishes CD39(+) taNK cells as a targetable effector population for immunotherapy.

Author Info: (1) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (2) Aix Marseille UniversitŽ, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy

Author Info: (1) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (2) Aix Marseille UniversitŽ, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France. (3) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (4) Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany. (5) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (6) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (7) Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany. (8) Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany. (9) Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany. (10) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (11) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (12) Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland. (13) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (14) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (15) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. Roche Pharma Research and Early Development pRED, Roche Innovation Center, Basel, Switzerland. (16) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (17) Department of Thoracic Surgery, University Hospital Basel, Basel, Switzerland. (18) Department of Thoracic Surgery, University Hospital Basel, Basel, Switzerland. (19) German Centre for Lung Diseases (DZL), BREATH site, Hannover, Germany. Institute of Pathology, Hannover Medical School, Hannover, Germany. (20) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. Medical Oncology, University Hospital Basel, Basel, Switzerland. (21) Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland. (22) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (23) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (24) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (25) Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland. (26) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. Medical Oncology, University Hospital Basel, Basel, Switzerland. (27) Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany. German Centre for Lung Diseases (DZL), BREATH site, Hannover, Germany. German Centre for Infection Research (DZIF), TTU-IICH, Hannover/Braunschweig site, Hannover, Germany. (28) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. (29) Aix Marseille UniversitŽ, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France. APHM, H™pital de la Timone, Marseille-Immunop™le Profiling Platform, Marseille, France. Paris-Saclay Cancer Cluster, Villejuif, France. Ecole Polytechnique, Palaiseau, France. (30) Roche Pharma Research and Early Development pRED, Roche Innovation Center, Basel, Switzerland. (31) Department of Biomedicine, University Hospital and University of Basel, Basel, Switzerland. Medical Oncology, University Hospital Basel, Basel, Switzerland.

Pan-cancer single-cell atlases of mouse and human tumor-associated dendritic cells Spotlight 

Caro and Kancheva et al. generated comprehensive scRNAseq atlases of tumor-associated mononuclear phagocytes (monocytes, macrophages, DCs, and pDCs) across 14 mouse and 10 human cancer settings. 31 murine and 25 human lineage-defined tumor-associated DC (TADC) subsets were identified across cDC1, cDC2A, cDC2B, and DC3 subsets. In tumors, new clusters emerged early on, and as tumors progressed, TADCs adopted a more inflammatory, but eventually less mature phenotype Tumor-mediated reprogramming was also evident within lymph node DCs. In patient data, certain TADC subsets and states were associated with patient outcomes.

Contributed by Lauren Hitchings

Caro and Kancheva et al. generated comprehensive scRNAseq atlases of tumor-associated mononuclear phagocytes (monocytes, macrophages, DCs, and pDCs) across 14 mouse and 10 human cancer settings. 31 murine and 25 human lineage-defined tumor-associated DC (TADC) subsets were identified across cDC1, cDC2A, cDC2B, and DC3 subsets. In tumors, new clusters emerged early on, and as tumors progressed, TADCs adopted a more inflammatory, but eventually less mature phenotype Tumor-mediated reprogramming was also evident within lymph node DCs. In patient data, certain TADC subsets and states were associated with patient outcomes.

Contributed by Lauren Hitchings

ABSTRACT: Dendritic cells (DCs) are critical inducers of anti-tumor immunity. To achieve a comprehensive mapping of mouse and human DC subsets and states in a cancer context, here we generate pan-cancer mouse and human tumor-associated DC (TADC) scRNA-seq atlases, encompassing 14 mouse tumor models and 10 human cancer types, within which we identify several lineage-defined DC subsets along with maturation/functional states. We show that TADCs acquire an inflammatory profile with tumor progression and that tumor-mediated reprogramming occurs within the DCs from lymph nodes of tumor-bearing mice. Importantly, we demonstrate that TADCs are broadly conserved between mice and humans, although species-specific differences may exist in some subsets and states. Moreover, we present a comprehensive assessment of how different human TADC clusters associate with patient survival outcomes. Overall, we provide an in-depth characterization of the TADC compartment in mouse and human cancers, which can improve our understanding of the tumor microenvironment and contribute to the development of new anti-cancer therapies.

Author Info: (1) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for I

Author Info: (1) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. Lab of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium. (2) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. (3) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. (4) Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium. Laboratory for Translational Genetics, VIB Center for Cancer Biology, Leuven, Belgium. (5) Laboratory for ER Stress and Inflammation, VIB Center for Inflammation Research, Ghent, Belgium. Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium. Laboratory for Barriers in Inflammation, VIB Center for Inflammation Research, Ghent, Belgium. Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. (6) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. (7) Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. VIB Single Cell Core; VIB, Ghent-Leuven, Belgium. (8) Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland. Agora Cancer Research Center, Lausanne, Switzerland. (9) Laboratory of Cancer Signaling, GIGA-Institute, University of Lige, Lige, Belgium. (10) Laboratory of Cancer Signaling, GIGA-Institute, University of Lige, Lige, Belgium. (11) Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. (12) Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. VIB Single Cell Core; VIB, Ghent-Leuven, Belgium. (13) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. (14) Laboratory of Cancer Signaling, GIGA-Institute, University of Lige, Lige, Belgium. Laboratory of Metabolic Regulation, GIGA-Institute, University of Lige, Lige, Belgium. (15) Laboratory of Cancer Signaling, GIGA-Institute, University of Lige, Lige, Belgium. WELBIO Department, WEL Research Institute, Wavre, Belgium. (16) Laboratory for ER Stress and Inflammation, VIB Center for Inflammation Research, Ghent, Belgium. Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium. (17) Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland. Agora Cancer Research Center, Lausanne, Switzerland. (18) Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium. Laboratory for Translational Genetics, VIB Center for Cancer Biology, Leuven, Belgium. (19) Lab of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium. (20) Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium. dlaoui@vub.be. Lab of Cellular and Molecular Immunology, Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium. dlaoui@vub.be.

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

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