Journal Articles

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

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.

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

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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.

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

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

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

Contributed by Shishir Pant

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

Contributed by Shishir Pant

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

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

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

Pan-cancer spatial atlas of tertiary lymphoid structures Spotlight 

Cho et al. integrated whole-section (WS) spatial transcriptomics across 12 cancer types to construct a pan-cancer tertiary lymphoid structure (TLS) atlas. TLSs spanned early, primary, and secondary maturation states with distinct spatial niches and immune organization. Tumor regions proximal to intratumoral TLSs showed enriched antigen-presentation and IFN-response programs, and reduced proliferative and EMT signatures. An AI framework trained on whole-slide H&E images classified TLS maturation and maturation-aware TLS composite scores, which stratified survival and treatment response, outperforming conventional TLS metrics.

Contributed by Shishir Pant

Cho et al. integrated whole-section (WS) spatial transcriptomics across 12 cancer types to construct a pan-cancer tertiary lymphoid structure (TLS) atlas. TLSs spanned early, primary, and secondary maturation states with distinct spatial niches and immune organization. Tumor regions proximal to intratumoral TLSs showed enriched antigen-presentation and IFN-response programs, and reduced proliferative and EMT signatures. An AI framework trained on whole-slide H&E images classified TLS maturation and maturation-aware TLS composite scores, which stratified survival and treatment response, outperforming conventional TLS metrics.

Contributed by Shishir Pant

ABSTRACT: Tertiary lymphoid structures (TLSs) are critical regulators of antitumor immunity, yet their spatial organization, maturation, and clinical relevance remain incompletely defined across cancers. We analyzed spatial transcriptomics spanning 12 cancer types to construct a pan-cancer TLS atlas and characterized TLS spatial architecture and maturation states. TLS maturation was accompanied by coordinated remodeling of distinct niche cell populations and distance-dependent gradients in tumor programs, orthogonally supported by ultrahigh-plex single-cell spatial profiling. To enable scalable TLS profiling, we trained an artificial intelligence framework that predicts TLS maturation states directly from hematoxylin and eosin-stained images and evaluated it across TCGA and independent therapy cohorts. We further derived a maturation-aware composite score capturing intratumoral TLS state composition, which robustly stratifies patients across cancer and treatment contexts, outperforming conventional TLS metrics.

Author Info: (1) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Genomic Medicine, The University of Texas MD Anderson Can

Author Info: (1) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (3) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (4) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (5) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. (6) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (7) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (8) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (9) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (10) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (11) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (12) Laura and Isaac Perlmutter Cancer Center, Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA. (13) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (14) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (15) Department of Biostatistics, The University of North Carolina, Chapel Hill, NC, USA. Department of Genetics, The University of North Carolina, Chapel Hill, NC, USA. (16) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (17) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (18) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (19) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (20) Therapeutics Discovery Division, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (21) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (22) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (23) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (24) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (25) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (26) Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (27) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (28) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (29) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (30) Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (31) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. (32) Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (33) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (34) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (35) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (36) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (37) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (38) Laura and Isaac Perlmutter Cancer Center, Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA. Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA. (39) The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (40) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (41) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (42) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Center for Cellular Language Intelligence, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Reprogramming CAR with cytokine signaling increases the efficacy of CAR-T cell therapy in solid tumour treatment and confers sustained immune memory Spotlight 

To improve CAR T cell efficacy for solid tumors, Sun and Liu et al. designed a series of CARs that enabled antigen-dependent “cytokine” co-activation, while preserving second-generation CAR structure. Incorporating compact IL-2/IL-15 receptor (IL2RB)-derived STAT5 docking motifs (Y392 and Y510) within the CD3ζITAM2/3 regions resulted in antigen-specific co-activation upon CAR engagement. The best candidate S71 CAR exhibited superior efficacy and dose-dependent memory in multiple xenograft tumor models (EDB-fibronectin, CD19, and CLDN), improved mitochondrial function, and supported durable and persistent T cell activity, with less exhaustion.

Contributed by Katherine Turner

To improve CAR T cell efficacy for solid tumors, Sun and Liu et al. designed a series of CARs that enabled antigen-dependent “cytokine” co-activation, while preserving second-generation CAR structure. Incorporating compact IL-2/IL-15 receptor (IL2RB)-derived STAT5 docking motifs (Y392 and Y510) within the CD3ζITAM2/3 regions resulted in antigen-specific co-activation upon CAR engagement. The best candidate S71 CAR exhibited superior efficacy and dose-dependent memory in multiple xenograft tumor models (EDB-fibronectin, CD19, and CLDN), improved mitochondrial function, and supported durable and persistent T cell activity, with less exhaustion.

Contributed by Katherine Turner

ABSTRACT: Chimeric antigen receptor (CAR) T-cell therapy has shown remarkable efficacy in hematologic malignancies but remains limited in solid tumors because of the immunosuppressive microenvironment, tumor heterogeneity, poor immune-cell infiltration, and progressive T-cell dysfunction. Because cytokine costimulation is critical for maintaining T-cell fitness, we developed a modular engineering strategy, distinct from previous approaches based on direct insertion of large cytokine receptor fragments, in which the intracellular CAR signaling domain was reconstructed to incorporate compact IL-2/IL-15 receptor-derived activation motifs, thereby enabling antigen-dependent coactivation while preserving the overall architecture of the parental CAR. Through systematic screening, we identified S71 as the optimal construct, with significantly greater antitumor activity than other mutants across multiple solid and hematologic tumor targets. Mechanistically, S71 rewired CAR signaling and reprogrammed tumor-induced metabolic responses through a self-sustaining mechanism, improving mitochondrial function and supporting durable T-cell activity. Functionally, S71 promoted enhanced persistence and robust immune memory responses against solid tumors. These findings demonstrate that modular integration of cytokine signaling motifs into CAR intracellular domains can improve CAR T-cell fitness and antitumor efficacy, and they establish S71 as a promising strategy for overcoming barriers to CAR T-cell therapy in solid tumors.

Author Info: (1) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (2) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (3) China Pharma

Author Info: (1) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (2) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (3) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (4) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (5) China Pharmaceutical University China. ROR: https://ror.org/01sfm2718 (6) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (7) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (8) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (9) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (10) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (11) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (12) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718 (13) China Pharmaceutical University Nanjing China. ROR: https://ror.org/01sfm2718

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