Journal Articles

Integration of donor microbiota following FMT correlates with anti-PD-1 response in melanoma

Using data from three trials of FMT plus anti-PD-1 in melanoma, Fessler et al. performed a strain-resolved metagenomic meta-analysis, and found that while neither microbial diversity nor acquisition of specific bacterial species were associated with response, recipient acquisition of the donor microbiome and microbiome community stability were. Further, while non-responders were enriched for pro-inflammatory and pathogen-associated secretion system genes, responders were enriched for functions of community-level metabolism and communication, highlighting the importance of the microbial ecosystem over species richness or specific species.

Contributed by Lauren Hitchings

Using data from three trials of FMT plus anti-PD-1 in melanoma, Fessler et al. performed a strain-resolved metagenomic meta-analysis, and found that while neither microbial diversity nor acquisition of specific bacterial species were associated with response, recipient acquisition of the donor microbiome and microbiome community stability were. Further, while non-responders were enriched for pro-inflammatory and pathogen-associated secretion system genes, responders were enriched for functions of community-level metabolism and communication, highlighting the importance of the microbial ecosystem over species richness or specific species.

Contributed by Lauren Hitchings

ABSTRACT: Fecal microbiota transplantation (FMT) has shown promise in improving anti-PD-1 therapy in melanoma, but the underlying microbial features remain poorly defined. We performed a strain-resolved metagenomic meta-analysis across three independent FMT plus anti-PD-1 melanoma trials (n_=_41). Across cohorts, therapeutic benefit was linked to successful integration of donor microbiota, rather than increased diversity or engraftment of specific species. Responders acquired more donor-derived strains, exhibited greater post-FMT similarity to their donor, and maintained a more stable microbiome. Following FMT, non-responders' microbiomes showed greater taxonomic instability, larger fluctuations in estimated microbial load, and increased abundance of pathogen-associated secretion system genes, whereas responders showed enrichment for microbial functions involved in community-level metabolism and communication. Finally, shifts in tumor-infiltrating immune profiles tracked with clinical outcomes and microbiome changes. Together these findings highlight that distinct patterns of microbiome restructuring, including stable community transitions and altered functional capacity, are associated with anti-PD-1 response following FMT.

Author Info: (1) Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA. (2) Department of Integrative Physiology, University of Colorado Boulder,

Author Info: (1) Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA. (2) Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA. (3) Department of Pathology, Stanford University, Stanford, CA, USA. Stanford Cancer Institute, Stanford University, Palo Alto, CA, USA. (4) Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA. jsonnenburg@stanford.edu. Chan Zuckerberg Biohub, San Francisco, CA, USA. jsonnenburg@stanford.edu. Center for Human Microbiome Studies, Stanford University School of Medicine, Stanford, CA, USA. jsonnenburg@stanford.edu.

Immunoediting restricts clonal neoantigens in primary, treatment-naive human tumors

To investigate immunoediting in human tumors, Borden et al. analyzed primary, treatment-naive cSCC tumors, which frequently arise in immunosuppressed patients following solid organ transplant, suggesting immune involvement. Compared to tumors from immunodeficient patients or just poorly infiltrated tumors, tumors from immunocompetent patients with high infiltration showed lower overall and clonal mutation burdens, and a lower frequency of variant alleles with high predicted neoantigen:MHC-I binding affinity. Further, neoantigens that shared features with validated immunogenic neoantigens were decreased in clonal versus subclonal cancer cells.

Contributed by Lauren Hitchings

To investigate immunoediting in human tumors, Borden et al. analyzed primary, treatment-naive cSCC tumors, which frequently arise in immunosuppressed patients following solid organ transplant, suggesting immune involvement. Compared to tumors from immunodeficient patients or just poorly infiltrated tumors, tumors from immunocompetent patients with high infiltration showed lower overall and clonal mutation burdens, and a lower frequency of variant alleles with high predicted neoantigen:MHC-I binding affinity. Further, neoantigens that shared features with validated immunogenic neoantigens were decreased in clonal versus subclonal cancer cells.

Contributed by Lauren Hitchings

ABSTRACT: T cell targeting of cancer cells alters the tumor antigen landscape in preclinical models. Here, we examined the impact of immunoediting on the antigenic landscape of primary, treatment-naive human tumors. Cutaneous squamous cell carcinoma tumors from immunocompetent and immunosuppressed patients revealed consistent tumor mutational signatures; however, high-immune-infiltrate tumors from immunocompetent patients had lower overall mutational burdens and lower clonal mutational burdens compared with low-infiltrate tumors from immunocompetent patients and tumors from immunosuppressed patients. The lower clonal mutational burden in high-immune-infiltrate tumors from immunocompetent patients persisted after accounting for tumor purity and growth rate. Predicted neoantigen: major histocompatibility complex (MHC) class I binding affinity decreased with increasing variant allele frequency, demonstrating restriction of mutations encoding MHC-binding neoantigens. Neoantigens with features shared with validated immunogenic neoantigens were decreased in clonal relative to subclonal cancer cell populations in high-immune-infiltrate tumors from immunocompetent patients. Thus, the immune system restricts cancer cells expressing immunogenic antigens from clonal populations in primary, treatment-naive human tumors.

Author Info: (1) Department of Dermatology, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, Phoenix, AZ 85012, USA. (2)

Author Info: (1) Department of Dermatology, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, Phoenix, AZ 85012, USA. (2) Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA. (3) Department of Dermatology, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ 85004, USA. (4) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (5) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (6) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (7) Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Champaign, IL 61801, USA. (8) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (9) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (10) Department of Dermatology, Mayo Clinic Health System, Scottsdale, AZ 85259, USA. (11) Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA. (12) BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA; R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85724, USA. (13) School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA. (14) School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA. (15) Department of Dermatology, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, Phoenix, AZ 85012, USA; University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85719, USA. Electronic address: khasting@arizona.edu.

MAGE-A4/MAGE-A8-targeted TCR-based bispecific T cell engager in recurrent and/or refractory solid tumors: a phase 1 trial

In a phase 1 trial, 61 patients with advanced solid tumors were treated with a TCE comprising (1) a high-affinity TCR binder for a shared MAGE-A4/MAGE-A8 CTA peptide presented on HLA-A*02:01, (2) a humanized, low(er)-affinity anti-TCRαβ/CD3 antibody for T cell binding and activation, and (3) a silenced Fc domain to extend half-life. 12 patients also received pembrolizumab. Median serum half-life was ~15d, an MTD was not reached, and a RP2D was determined. TRAEs were manageable (often CRS, lymphopenia, or neutropenia) and the ORR was 14% in evaluable patients. Pembrolizumab did not significantly affect safety or response rates.

Contributed by Alex Najibi

In a phase 1 trial, 61 patients with advanced solid tumors were treated with a TCE comprising (1) a high-affinity TCR binder for a shared MAGE-A4/MAGE-A8 CTA peptide presented on HLA-A*02:01, (2) a humanized, low(er)-affinity anti-TCRαβ/CD3 antibody for T cell binding and activation, and (3) a silenced Fc domain to extend half-life. 12 patients also received pembrolizumab. Median serum half-life was ~15d, an MTD was not reached, and a RP2D was determined. TRAEs were manageable (often CRS, lymphopenia, or neutropenia) and the ORR was 14% in evaluable patients. Pembrolizumab did not significantly affect safety or response rates.

Contributed by Alex Najibi

ABSTRACT:IMA401 is a T cell receptor (TCR)-based next-generation bispecific T cell engaging receptor (TCER) targeting an HLA-A*02:01-presented peptide derived from MAGE-A4/MAGE-A8 with its high-affinity TCR-based domain, incorporating a low-affinity T-cell-recruiting domain and an optimized Fc domain to prolong half-life. In this prespecified interim analysis of a phase 1 first-in-human trial, 61 patients with advanced solid tumors received intravenous IMA401 (0.0066 mg-2.5 mg) with or without pembrolizumab. The primary endpoint was determination of the maximum tolerated dose (MTD) and/or recommended phase 2 dose (RP2D) of IMA401 monotherapy and in combination with pembrolizumab. Secondary objectives included safety and tolerability, antitumor activity and pharmacokinetics. The MTD was not reached as defined by the clinical trial protocol, and the RP2D was 1-2 mg IMA401 biweekly. Treatment-related adverse events (TRAEs) were well manageable; the most common any-grade TRAEs were cytokine release syndrome (38%, grades 1-2 only), transient lymphopenia (33%) and reversible neutropenia (31%). Five patients experienced dose-limiting toxicity (DLT) events primarily related to neutropenia. No further DLTs occurred in the RP2D range with dexamethasone premedication. One possibly-related death (pneumonia in a patient with rapidly progressing lung metastases) was reported outside RP2D at 2.5 mg IMA401. In the overall efficacy-evaluable population across all dose levels (n = 56), including low starting doses (from 0.0066 mg), the confirmed objective response rate (ORR) was 14% (8/56). In patients receiving IMA401 at the RP2D, an ORR of 20% (8/41) was observed across 15 different indications (post hoc analysis). In the largest subgroup of patients treated at RP2D, namely head and neck cancer, the ORR was 29% (4/14) with a median duration of response of 8.8 months. These findings show that the bispecific TCER platform has a manageable safety profile with mostly transient adverse events and promising antitumor activity at the RP2D of IMA401 with or without pembrolizumab. ClinicalTrials.gov identifier: NCT05359445 .

Author Info: (1) NCT/UCC Early Clinical Trial Unit and Department of Medicine I, Dresden University of Technology, Dresden, Germany. (2) CharitŽ UniversitŠtsmedizin Berlin, Berlin, Germany. (3)

Author Info: (1) NCT/UCC Early Clinical Trial Unit and Department of Medicine I, Dresden University of Technology, Dresden, Germany. (2) CharitŽ UniversitŠtsmedizin Berlin, Berlin, Germany. (3) National Center for Tumor Diseases, Heidelberg, Germany. (4) Department of Hematology, Oncology, and Stem Cell Transplantation, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany. (5) Department of Medicine A for Hematology, Oncology and Pneumology, University Hospital Muenster, Muenster, Germany. (6) National Center for Tumor Diseases, Heidelberg, Germany. Thoraxklinik Heidelberg gGmbH, University Hospital Heidelberg, Heidelberg, Germany. (7) University Hospital WŸrzburg, Comprehensive Cancer Center Mainfranken, WŸrzburg, Germany. (8) Marien Hospital DŸsseldorf, DŸsseldorf, Germany. (9) Department of Internal Medicine III, Klinikum Chemnitz, Chemnitz, Germany. (10) Department of Medicine III, Technical University of Munich (TUM), Klinikum rechts der Isar, School of Medicine and Health, Munich, Germany. TranslaTUM, Center for Translational Cancer Research, Technical University of Munich (TUM), Munich, Germany. (11) University Hospital of TŸbingen, TŸbingen, Germany. (12) University Hospital Regensburg, Regensburg, Germany. (13) Immatics Biotechnologies GmbH, TŸbingen, Germany. (14) Immatics Biotechnologies GmbH, TŸbingen, Germany. (15) Immatics Biotechnologies GmbH, TŸbingen, Germany. (16) Immatics Biotechnologies GmbH, TŸbingen, Germany. (17) Immatics Biotechnologies GmbH, TŸbingen, Germany. (18) Immatics Biotechnologies GmbH, TŸbingen, Germany. (19) Immatics Biotechnologies GmbH, TŸbingen, Germany. (20) Immatics Biotechnologies GmbH, TŸbingen, Germany. (21) University Hospital Bonn, Bonn, Germany. (22) Nuremberg General Hospital, Nuremberg, Germany. (23) Department of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Ulm, Germany. (24) University Hospital, Goethe University Frankfurt, Frankfurt Cancer Institute, Frankfurt, Germany. (25) University Hospital Erlangen, Erlangen, Germany. (26) Immatics Biotechnologies GmbH, TŸbingen, Germany. (27) Immatics Biotechnologies GmbH, TŸbingen, Germany. Carsten.Reinhardt@immatics.com.

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

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

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

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

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.

FAP-CD40 and PD1-IL2v combination therapy reprograms immunologically cold tumors through de novo intratumoral T cell-dendritic cell clusters Spotlight 

In a KPC tumor model, Nguyen et al. combined a FAP-targeted CD40 agonist (FAP-CD40; localizes CD40 stimulation to the TME) and PD1–IL-2v (targets a mutated IL-2 to PD-1+ T cells and not Tregs). FAP-CD40 alone activated TME cDC1s, which migrated to tdLNs. Combination therapy expanded TME T cells and increased CD4+/CD8+/cDC1 clustering and therapeutic efficacy (dependent on both CD4+ and CD8+ T cells) compared to monotherapies. FTY720 blockade of LN egress did not preclude clustering or efficacy, suggesting activation of TME T cells. Combination therapy boosted TME T cell Th1 gene expression, TNFα/IFNγ production, and Nur77 promoter activity.

Contributed by Alex Najibi

In a KPC tumor model, Nguyen et al. combined a FAP-targeted CD40 agonist (FAP-CD40; localizes CD40 stimulation to the TME) and PD1–IL-2v (targets a mutated IL-2 to PD-1+ T cells and not Tregs). FAP-CD40 alone activated TME cDC1s, which migrated to tdLNs. Combination therapy expanded TME T cells and increased CD4+/CD8+/cDC1 clustering and therapeutic efficacy (dependent on both CD4+ and CD8+ T cells) compared to monotherapies. FTY720 blockade of LN egress did not preclude clustering or efficacy, suggesting activation of TME T cells. Combination therapy boosted TME T cell Th1 gene expression, TNFα/IFNγ production, and Nur77 promoter activity.

Contributed by Alex Najibi

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a major challenge for immunotherapy due to its immunologically cold tumor nature, characterized by poor T cell infiltration and a highly suppressive tumor microenvironment. Here, we propose a novel strategy, combining fibroblast activation protein (FAP)-CD40 to activate dendritic cells (DCs) in the tumor microenvironment and programmed cell death protein-1 (PD1)-interleukin 2v (IL2v) to promote the expansion and differentiation of tumor-infiltrating T cells. We hypothesize that this combination will synergistically enhance both T cell priming and expansion directly within pancreatic 4662 KPC tumors, which recapitulate the immunologically cold features of human PDAC. METHODS: Immune cell distribution and abundance following FAP-CD40/PD1-IL2v monotherapy or combination therapy were analyzed using multiplexed confocal imaging (3D immune phenotyping). FTY720 studies assessed the contribution of lymph node priming in treatment efficacy, while CD4+/CD8+ T cell depletion experiments identified the roles of these subsets in combination therapy. T cell functionality was further assessed through ex vivo restimulation assays and single-cell RNA sequencing. RESULTS: Combination therapy induced dense intratumoral clusters of CD4(+) and CD8(+) T cells, colocalized with type 1 conventional DCs, termed as T cell-DC clusters (TDCs). These TDCs were strongly associated with tumor regression, which required both CD4(+) and CD8(+) T cells. Furthermore, T cells from combination-treated tumors showed enhanced functionality, with increased tumor necrosis factor-alpha and interferon-gamma production compared with monotherapy groups. Single-cell RNA sequencing revealed polarization of CD4(+) T cells toward a T helper cell 1 phenotype in combination-treated tumors. CONCLUSION: The combination of FAP-CD40 and PD1-IL2v offers a promising strategy for treating poorly infiltrated, cold tumors. By driving T cell infiltration, promoting de novo TDC formation and orchestrating local antitumor immunity, this strategy provides a foundation for future therapies targeting immunotherapy-resistant tumors.

Author Info: (1) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (2) Roche Pharma Research and Early Development, Roche Innovation Center Ba

Author Info: (1) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (2) Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland. (3) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (4) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (5) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (6) Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland. (7) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (8) Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland. (9) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (10) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (11) Institute of Experimental Immunology, UniversitŠt ZŸrich, ZŸrich, Switzerland. Department of Immunology, Heidelberg University Medical Faculty Mannheim, Mannheim, Germany. (12) Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland. (13) Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland leo.kunz@roche.com.

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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