Journal Articles

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

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.

Tumor irradiation promotes antigen dressing of dendritic cells to enhance CAR T cell persistence and efficacy in lung metastases Spotlight 

Navarre, Ishibashi, and Nair et al. showed that focal 8 Gy tumor irradiation in a syngeneic metastatic lung adenocarcinoma model enhanced CAR T cell persistence and efficacy in a DC-dependent manner. Irradiation conditioned tumor cells for trogocytic antigen transfer onto DCs and macrophages, but only DCs engaged CAR T cells through the chimeric receptor, sustaining their activity. DC depletion abolished sustained CAR T cells and long-term tumor control. CAR T cell expansion was restricted to irradiated tumors, and not adjacent antigen-expressing normal lung tissue, indicating spatially restricted DC–CAR T cell engagement.

Contributed by Shishir Pant

Navarre, Ishibashi, and Nair et al. showed that focal 8 Gy tumor irradiation in a syngeneic metastatic lung adenocarcinoma model enhanced CAR T cell persistence and efficacy in a DC-dependent manner. Irradiation conditioned tumor cells for trogocytic antigen transfer onto DCs and macrophages, but only DCs engaged CAR T cells through the chimeric receptor, sustaining their activity. DC depletion abolished sustained CAR T cells and long-term tumor control. CAR T cell expansion was restricted to irradiated tumors, and not adjacent antigen-expressing normal lung tissue, indicating spatially restricted DC–CAR T cell engagement.

Contributed by Shishir Pant

ABSTRACT: Metastatic solid tumors remain the principal cause of cancer mortality worldwide. High tumor burden impairs responses to chimeric antigen receptor (CAR) T cell therapy, yet off-tumor toxicity limits the doses that can be safely delivered. Strategies to selectively enhance CAR T cell activity at tumor sites could widen the therapeutic window. Using syngeneic models of extensive metastatic lung adenocarcinoma and melanoma, we show that 8_Gy of tumor irradiation significantly enhanced CAR T cell persistence in a manner critically dependent on dendritic cells (DCs). Irradiation promoted trogocytic antigen dressing of tumor antigens onto DCs, which then expanded CAR T cells through the chimeric receptor. Without functional DCs, irradiation failed to sustain CAR T cell persistence and tumors relapsed. Irradiation increased CAR T cell numbers within tumors but not in adjacent normal lung tissue that also expressed target antigen, conferring robust control of tumor without increased toxicity. These data define a mechanistic basis and rationale for combining radiotherapy with CAR T cell therapy.

Author Info: (1) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department

Author Info: (1) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Biomedical Engineering, The City College of New York, New York, NY, USA. (2) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. (3) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (4) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (5) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (6) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (7) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (8) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (9) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (10) Department of Medicine, Immunology Program, Gene Transfer and Somatic Cell Engineering Laboratory, Center for Cell Engineering and Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Columbia Initiative in Cell Engineering and Therapy (CICET), Cancer Cell Therapy Initiative in the Vagelos Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA. (11) Department of Medicine, Immunology Program, Gene Transfer and Somatic Cell Engineering Laboratory, Center for Cell Engineering and Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. (12) Department of Medicine, Immunology Program, Gene Transfer and Somatic Cell Engineering Laboratory, Center for Cell Engineering and Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Cluster of Excellence iFIT (EXC2180) 'Image-guided and Functionally Instructed Tumor Therapies', University Children's Hospital TŸbingen, TŸbingen, Germany. (13) Department of Medicine, Immunology Program, Gene Transfer and Somatic Cell Engineering Laboratory, Center for Cell Engineering and Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Columbia Initiative in Cell Engineering and Therapy (CICET), Cancer Cell Therapy Initiative in the Vagelos Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA. (14) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (15) Department of Dermatology, University of Wisconsin-Madison, Madison, WI, USA. (16) Department of Medicine-Division of Hematology and Oncology, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, University of California, San Francisco, CA, USA. (17) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (18) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (19) Columbia Initiative in Cell Engineering and Therapy (CICET), Cancer Cell Therapy Initiative in the Vagelos Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA. (20) Department of Immunology and Immunotherapy, Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. jalal.ahmed@mountsinai.org. Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. jalal.ahmed@mountsinai.org.

Differential assembly of mouse and human tumor microenvironments Spotlight 

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

Contributed by Shishir Pant

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

Contributed by Shishir Pant

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

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

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

Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery Featured  

Using high-throughput yeast display and protein language models (pLMs), Wang, Yeh, et al. developed a new approach to determine TCR recognition that goes beyond the TCR sequence. This method generates deep peptide recognition profiles (PRPs), with PRP functional distance predicting the specificity of a new TCR, thereby enabling the discovery of novel candidate autoantigens in autoimmune disease.

Using high-throughput yeast display and protein language models (pLMs), Wang, Yeh, et al. developed a new approach to determine TCR recognition that goes beyond the TCR sequence. This method generates deep peptide recognition profiles (PRPs), with PRP functional distance predicting the specificity of a new TCR, thereby enabling the discovery of novel candidate autoantigens in autoimmune disease.

ABSTRACT: Predicting T cell receptor (TCR) specificity on the basis of sequence is challenging because TCRs of similar sequence can recognize entirely different antigens, whereas TCRs of different sequence can recognize the same antigens. Here we present a system that integrates high-throughput yeast display with fine-tuned protein language models (pLMs) to generate deep peptide recognition profiles (PRPs) for individual TCRs, each detailing binding against millions of peptides. We provide detailed PRPs for a panel of HLA-B*27:05-restricted TCRs from persons with ankylosing spondylitis and acute anterior uveitis that almost exclusively recognize peptides through CDR3β. pLMs trained on these PRPs outperform AlphaFold3 and tFold-TCR in predicting T cell activation. We discover and validate novel candidate autoantigens, demonstrate that model generalization to new TCRs correlates with functional distance (PRP divergence) rather than sequence similarity and introduce a model-intrinsic uncertainty metric to quantify prediction confidence. This system and its associated PRP datasets offer a scalable approach to mapping TCR recognition, accelerating antigen discovery and guiding TCR engineering.

Author Info: (1) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. Howard Hughes Medical Institute, Stanford University School of Medic

Author Info: (1) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. (2) Biohub, Chicago, IL, USA. Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA. Medical Scientist Training Program, University of Chicago, Chicago, IL, USA. (3) Biohub, Chicago, IL, USA. (4) Biohub, Chicago, IL, USA. (5) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. (6) Rheumatology Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA. (7) Rheumatology Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA. (8) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. (9) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. (10) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. (11) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. (12) Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (13) Rheumatology Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA. (14) Biohub, Chicago, IL, USA. aakhan@uchicago.edu. Departments of Pathology, and Family Medicine, University of Chicago, Chicago, IL, USA. aakhan@uchicago.edu. (15) Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. kcgarcia@stanford.edu. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. kcgarcia@stanford.edu. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA. kcgarcia@stanford.edu.

Tumors hijack immune-privileging regulons via distinct cell types to confer T cell desertion and immunotherapy resistance across various cancers Spotlight 

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

ABSTRACT: Immune checkpoint blockade (ICB) has transformed oncology, yet most patients fail to respond, suffer from hyper-progressive disease, or face severe immune-related toxicities, underscoring the urgent need for biomarkers that identify non-responders. Here we show that tumors co-opt an immune-privileging regulon signature (IMPREG) mirroring transcriptional programs of immune-privileged organs - to enforce T-cell desertion and ICB resistance across solid tumor types. Single-cell and spatial transcriptomic analyses reveal that tumors activate IMPREG through three distinct cellular routes: malignant cells adopting immature neuronal states, cancer-associated fibroblasts assuming myofibroblast identities, or endothelial cells - each creating localized niches of immune suppression and antigen-presentation collapse. Across 4 discovery and 36 validation clinical datasets, IMPREG consistently predicts immunotherapy resistance in 14 distinct cancer types, functioning as an orthogonal marker independent of established biomarkers. Crucially, IMPREG-expressing tumors show enhanced sensitivity to EGFR inhibitors or anti-angiogenic therapies in specific tumor entities. These findings suggest IMPREG as a dual-utility predictive biomarker for personalized treatment stratification.

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC,

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA. 4UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu. 5Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu.

CD39+CD49a+CD103+ cytotoxic tissue-resident natural killer cells infiltrate and control solid epithelial tumor growth in mice

Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Human tissue-resident natural killer (NK) cells (trNK cells), broadly defined by markers of tissue residency, such as CD49a [integrin α1 (ITGA1)] and CD103 [integrin αE (ITGAE)], are increasingly recognized for their immunoregulatory role in host control of infection, malignancy, and autoimmunity. Although the importance of transforming growth factor-β in trNK cell differentiation has been demonstrated, the context in which the differentiation of CD49a+CD103+ trNK cells occurs can result in either an immunosuppressive phenotype (e.g., decidual NK cells) or a highly cytotoxic one (e.g., some tumor trNK subsets). To understand this dichotomy better, we used a multiomic approach to molecularly characterize these cells. We identified a cytotoxic trNK (ctrNK) cell population, characterized by the expression of CD39. These ctrNK cells exhibited superior cytolytic activity against tumor target cells, enhanced capacity to infiltrate into solid tumor microenvironments, and augmented ability to control solid tumor growth in vivo compared with conventionally activated peripheral NK cells. This heightened cytolytic and infiltrative functionality of ctrNK cells appeared to be conferred, in part, by the expression of CD103 and by avidity for tumor targets. Because adoptive immune cell therapy of solid tumor malignancies has been challenged by the inefficiency of ex vivo expanded immune cells to infiltrate immunosuppressive solid tumor microenvironments, our observations that ctrNK cells can be differentiated and expanded ex vivo present a potential platform for adoptive cell therapy of solid tumor malignancies.

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanfo

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. 3Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA. 4Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. 5Pelotonia Institute for Immuno-Oncology, Ohio State University Comprehensive Cancer Center-the James, Columbus, OH 43210, USA. 6Department of Molecular Medicine and Therapeutics, College of Medicine, Ohio State University, Columbus, OH 43210, USA. 7Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. 8Division of Infectious Diseases, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. 9Section of Computational Biology, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. 10Siteman Cancer Center at WashU Medicine, St. Louis, MO 63110, USA.

Integrated Single-Cell Profiling Reveals Dichotomous NK Cell Populations Associated with Immunosuppression in Solid Tumors Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Natural killer (NK) cells represent key effectors of antitumor immunity, yet emerging evidence highlights populations with distinct roles in cancer. Despite such expanded diversity within the NK cell repertoire, we lack an understanding of how this heterogeneity impacts immune responses and downstream clinical outcomes. Using single-cell RNA-sequencing (scRNA-seq), we systematically profiled NK cells across cancer and uncovered a dichotomous phenotypic and functional landscape of tumor-infiltrating NK cells shaped by opposing intrinsic signaling programs that drive the expression of IFNG or TGFB1. These divergent programs are associated with distinct transcription factor circuits that integrate cues within the tumor microenvironment and skew NK cells towards pro-inflammatory or suppressive functions. We found that the capacity for NK cells to engage in either functional direction is intrinsically linked to their phenotypic identity. Canonical NK cells recruited from circulation predominantly directed suppressive TGFB1 signals towards effector CD8+ T cells in tumors. Of note, these subsets exhibited higher TGFB1 expression than intratumoral myeloid cells across tumor types. In contrast, a tissue-resident adaptive subset exhibited exclusively pro-inflammatory IFNG-driven profiles and was associated with prolonged survival in both primary and metastatic tumor settings. Moreover, these tissue-resident adaptive NK cells, but not other subsets, were linked to response to immune checkpoint blockade. Collectively, our study reveals a previously unrecognized regulatory axis in NK cells that shapes NK cell diversity and augments broader antitumor immune responses.

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United Stat

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United States. 4Caris Life Sciences (United States) Phoenix, AZ United States. 5University of Minnesota Minnesota, MN United States. 6University of Minnesota Minneapolis, Minnesota United States. 7Mayo Clinic Rochester, MN United States. 8University of Chicago Chicago, IL United States. 9Caris Life Sciences (United States) Los Angeles, CA United States. 10University of Minnesota Minneapolis, MN United States.

Ferroptosis-armed dendritic cell vaccines for glioma immunotherapy Spotlight 

A prophylactic DC vaccine loaded with ferroptotic (iron-dependent cell death) glioma cell line lysates protected against glioma growth in mice, superior to immunogenic cell death (ICD) or freeze/thaw (non-ICD) lysates. The vaccine also mediated therapeutic efficacy, induced antigen-specific CTL responses in SLOs, and increased i.t. CTLs (particularly CD39+ effector-memory cells) compared to controls. Ferroptosis induced ICD markers on glioma cells, and blocking calreticulin or ATP, but not HMGB1, abrogated vaccine efficacy. Ferroptotic lysates activated DCs and displayed a unique proteomic profile, potentially presenting novel TAAs.

Contributed by Alex Najibi

A prophylactic DC vaccine loaded with ferroptotic (iron-dependent cell death) glioma cell line lysates protected against glioma growth in mice, superior to immunogenic cell death (ICD) or freeze/thaw (non-ICD) lysates. The vaccine also mediated therapeutic efficacy, induced antigen-specific CTL responses in SLOs, and increased i.t. CTLs (particularly CD39+ effector-memory cells) compared to controls. Ferroptosis induced ICD markers on glioma cells, and blocking calreticulin or ATP, but not HMGB1, abrogated vaccine efficacy. Ferroptotic lysates activated DCs and displayed a unique proteomic profile, potentially presenting novel TAAs.

Contributed by Alex Najibi

ABSTRACT: The type of cell death has proven to play a crucial role in cancer immunotherapy efficacy. Immunogenic cell death (ICD) enhances tumor adjuvanticity and antigenicity by releasing danger signals and altering the immune peptidome. The immunogenicity of ferroptosis, an iron-dependent form of cell death, remains uncertain. Here, we show that dendritic cell (DC) vaccines loaded with ferroptotic lysates protect mice against glioma growth, inducing IFN-_ production, and promoting robust CD8_ T cell infiltration, activation, and effector memory formation in the tumor microenvironment. The intrinsic immunogenicity of ferroptosis was independent of the glioma type and the ferroptosis inducer. Instead, it critically required the presence of the damage-associated molecular patterns calreticulin and ATP, rather than involving HMGB1-TLR4 signaling. However, supplementing these DAMPs into DC vaccines loaded with non-ICD lysates did not restore efficacy to the level of the ferroptosis-based DC vaccine, suggesting a more complex mechanism beyond a purely DAMP-mediated effect. These findings demonstrate that ferroptosis-loaded DC vaccines elicit a potent, tumor-specific immune response, capable of eradicating intracranial gliomas in mice, which highlights their potential in cancer immunotherapy.

Author Info: 1Cell Death Investigation and Therapy (CDIT) Laboratory, Anatomy and Embryology Unit, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent Unive

Author Info: 1Cell Death Investigation and Therapy (CDIT) Laboratory, Anatomy and Embryology Unit, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. 2Cancer Research Institute Ghent, Ghent, Belgium. 3Institute of Neurosciences, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny, Russia. 4Thoracic Tumor Immunology Laboratory (TTIL), Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Science, Ghent University, Ghent, Belgium. 5VIB Proteomics Core, VIB, Ghent, Belgium. 6VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium. 7Department of Biomolecular Medicine, Ghent University, Ghent, Belgium. 8myNEO Therapeutics, Ghent, Belgium. 9IBiTech-MEDISIP-Infinity Laboratory, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium. 10Clinical Cooperation Unit (CCU) Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 11Neurology Clinic, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany. 12Université Paris Cité, INSERM, CNRS, Institut Necker Enfants Malades, Paris, France. 13Service Immunologie Biologique, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France. 144Brain, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. 15Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny, Russia. 16VIB Center for Inflammation Research, Ghent, Belgium. 17Department of Biomedical Molecular Biology, Faculty of Sciences, Ghent University, Ghent, Belgium. 18Cell Death Investigation and Therapy (CDIT) Laboratory, Anatomy and Embryology Unit, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. elena.catanzaro@ugent.be. 19Cancer Research Institute Ghent, Ghent, Belgium. elena.catanzaro@ugent.be. #Contributed equally.

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