Journal Articles

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

(1) Di Pietro A (2) Au L (3) Crock P (4) Thio N (5) Pizzolla A (6) Nguyen TN (7) Macdonald S (8) Chalmers H (9) Zhu R (10) Airaghi A (11) Molden-Hauer T (12) Bacac M (13) Schwalie P (14) Schlenker R (15) Levesque MP (16) Mailer S (17) Barnes-Cullen K (18) Winch K (19) Chan J (20) Yeung GA (21) Spain L (22) Rao AD (23) Sandhu S (24) Gyorki DE (25) McArthur GA (26) Mackay LK (27) Neeson PJ

Nature communications

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

Contributed by Shishir Pant

(1) Di Pietro A (2) Au L (3) Crock P (4) Thio N (5) Pizzolla A (6) Nguyen TN (7) Macdonald S (8) Chalmers H (9) Zhu R (10) Airaghi A (11) Molden-Hauer T (12) Bacac M (13) Schwalie P (14) Schlenker R (15) Levesque MP (16) Mailer S (17) Barnes-Cullen K (18) Winch K (19) Chan J (20) Yeung GA (21) Spain L (22) Rao AD (23) Sandhu S (24) Gyorki DE (25) McArthur GA (26) Mackay LK (27) Neeson PJ

Nature communications

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

Contributed by Shishir Pant

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

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

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

Citation: Nat Commun 2026 Jun 11 Epub06/11/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42277002

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An ICAM1-targeting chimeric costimulatory receptor mimics the immune synapse and enhances tumor-specific T cell function

(1) Min IM (2) Yang Y (3) Stefanova D (4) Vedvyas Y (5) Babu DS (6) Lee DH (7) Alcaina Y (8) Riascos MC (9) Puc J (10) Chen KJ (11) Gonzalez-Valdivieso J (12) Thaiparambil J (13) Bilal M (14) He B (15) Burnett AC (16) Zarnegar R (17) Fahey TJ (18) Jin MM

Cancer immunology research

Min et al. developed an ICAM1-specific 4-1BB fusion protein. Engagement of this chimeric costimulatory receptor (ICCR) enhanced tumor cell conjugation and force-dependent immune synapse stability, triggered NF-κB-signaling, and amplified TCR-driven functional activation of engineered T cells, particularly against targets with lower antigen density. In a patient-derived orthotropic xenograft model of aggressive, incurable thyroid cancer, autologous ICCR+ T cells showed selective expansion and prolongation of survival. ICCR+ T cells exhibited reduced TCR diversity, and upregulation of cytotoxicity, TCR signaling, costimulation, and exhaustion genes.

Contributed by Ute Burkhardt

(1) Min IM (2) Yang Y (3) Stefanova D (4) Vedvyas Y (5) Babu DS (6) Lee DH (7) Alcaina Y (8) Riascos MC (9) Puc J (10) Chen KJ (11) Gonzalez-Valdivieso J (12) Thaiparambil J (13) Bilal M (14) He B (15) Burnett AC (16) Zarnegar R (17) Fahey TJ (18) Jin MM

Cancer immunology research

Min et al. developed an ICAM1-specific 4-1BB fusion protein. Engagement of this chimeric costimulatory receptor (ICCR) enhanced tumor cell conjugation and force-dependent immune synapse stability, triggered NF-κB-signaling, and amplified TCR-driven functional activation of engineered T cells, particularly against targets with lower antigen density. In a patient-derived orthotropic xenograft model of aggressive, incurable thyroid cancer, autologous ICCR+ T cells showed selective expansion and prolongation of survival. ICCR+ T cells exhibited reduced TCR diversity, and upregulation of cytotoxicity, TCR signaling, costimulation, and exhaustion genes.

Contributed by Ute Burkhardt

ABSTRACT: Engineered T cell therapies, such as chimeric antigen receptor (CAR) and T cell receptor (TCR)-based approaches, have transformed outcomes in hematological malignancies, yet their efficacy in solid tumors remains limited by tumor antigen escape, immunosuppressive microenvironments, and insufficient activation of CAR or TCR signaling. To overcome these barriers, we developed an intercellular adhesion molecule 1 (ICAM1)-specific chimeric costimulatory receptor (ICCR) engineered for expression in T cells to augment their activation. ICAM1 is broadly expressed across solid tumors and is further upregulated by IFN_ released during early T cell engagement, creating a feed-forward loop that reinforces tumor recognition. ICCR engagement with ICAM1 triggered NF_B signaling independently of TCR-p/MHC engagement; however, full T cell activation and cytotoxic function remained dependent on intact TCR signaling. In primary T cells, ICCR increased proliferation, cytokine production, and cytotoxicity, resulting in improved tumor control in two anaplastic thyroid cancer xenograft models treated with allogeneic or autologous ICCR-T cells. Mechanistically, ICCR strengthened tumor cell engagement, promoted selection and expansion of tumor-specific TCR clonotypes, and amplified downstream signaling pathways. These findings identify ICCR as a strategy that leverages an immune synapse-mimetic mechanism to enhance the function of low-activity tumor-specific TCRs and improve T cell responses in solid tumor microenvironments.

Author Info: (1) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (2) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (3) Weill Corn

Author Info: (1) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (2) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (3) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (4) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (5) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (6) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (7) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (8) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (9) AffyImmune Therapeutics, Inc. Natick, MA United States. (10) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (11) Weill Cornell Medicine New York, NY United States. ROR: https://ror.org/02r109517 (12) Houston Methodist Research Institute Houston, TX United States. (13) Weill Cornell Medicine New York, New York United States. ROR: https://ror.org/02r109517 (14) Weill Cornell Medicine New York, New York United States. ROR: https://ror.org/02r109517 (15) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171 (16) New York Presbyterian Hospital - Weill Cornell Medical College New York, NY United States. (17) Weill Cornell New York, NY United States. (18) Houston Methodist Houston, TX United States. ROR: https://ror.org/027zt9171

Citation: Cancer Immunol Res 2026 Apr 27 Epub04/27/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42043451

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Cuproptosis-immunity crosstalk informs strategy to overcome immunotherapy resistance

(1) Lei G (2) Lu Z (3) Xu Z (4) Braun C (5) Huo D (6) Gao J (7) Tan L (8) Hong T (9) Wu S (10) Sun M (11) Zhao X (12) Li Q (13) Chen X (14) Yan Y (15) Lee H (16) Mao C (17) Zhuang L (18) Ku LT (19) Puebla N (20) Barsoumian H (21) Yao J (22) Hong L (23) Zhang J (24) Tran H (25) Lee JJ (26) Gibbons D (27) Vaporciyan A (28) Heymach J (29) Lin C (30) Gottlieb E (31) You MJ (32) Welsh JW (33) Lin SH (34) Zang X (35) Li Z (36) Gan B

Cell

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

Contributed by Shishir Pant

(1) Lei G (2) Lu Z (3) Xu Z (4) Braun C (5) Huo D (6) Gao J (7) Tan L (8) Hong T (9) Wu S (10) Sun M (11) Zhao X (12) Li Q (13) Chen X (14) Yan Y (15) Lee H (16) Mao C (17) Zhuang L (18) Ku LT (19) Puebla N (20) Barsoumian H (21) Yao J (22) Hong L (23) Zhang J (24) Tran H (25) Lee JJ (26) Gibbons D (27) Vaporciyan A (28) Heymach J (29) Lin C (30) Gottlieb E (31) You MJ (32) Welsh JW (33) Lin SH (34) Zang X (35) Li Z (36) Gan B

Cell

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

Contributed by Shishir Pant

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

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

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

Citation: Cell 2026 Jun 22 Epub06/22/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42330950

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

(1) Fessler JL (2) Olm MR (3) Engleman EG (4) Sonnenburg JL

Nature communications

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

(1) Fessler JL (2) Olm MR (3) Engleman EG (4) Sonnenburg JL

Nature communications

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.

Citation: Nat Commun 2026 May 30 Epub05/30/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42218119

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

(1) Borden ES (2) Castellano D (3) Kantzos EA (4) Hughes A (5) Leibovit-Reiben Z (6) Stockard A (7) Choudhary A (8) Ma T (9) Li X (10) Mangold AR (11) Gutenkunst RN (12) LaFleur BJ (13) Buetow KH (14) Wilson MA (15) Hastings KT

Immunity

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

Contributed by Lauren Hitchings

(1) Borden ES (2) Castellano D (3) Kantzos EA (4) Hughes A (5) Leibovit-Reiben Z (6) Stockard A (7) Choudhary A (8) Ma T (9) Li X (10) Mangold AR (11) Gutenkunst RN (12) LaFleur BJ (13) Buetow KH (14) Wilson MA (15) Hastings KT

Immunity

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

Contributed by Lauren Hitchings

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

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

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

Citation: Immunity 2026 Jun 11 Epub06/11/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42276071

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

(1) Jassowicz L (2) Feng F (3) Warta R (4) Heinicke F (5) Wšllner A (6) Briesch H (7) Sor F (8) Soller J (9) Lotsch C (10) Wong JKL (11) Park CG (12) Aubert N (13) Scheer M (14) Hu Z (15) Knoll M (16) Schlegel J (17) Frank L (18) Dao Trong P (19) Jungwirth G (20) Nohman AI (21) Maa§ K (22) Grabe N (23) Barthel M (24) Butz S (25) Lulay C (26) Zucknick M (27) Bortolomeazzi M (28) Sant P (29) Thewes V (30) Michel L (31) Fremd C (32) Schneeweiss A (33) Sun C (34) Rippe K (35) Abdollahi A (36) von Deimling A (37) Unterberg A (38) Krieg SM (39) Mallm JP (40) Lichter P (41) Zapatka M (42) Seiffert M (43) Herold-Mende C

Cancer cell

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

(1) Jassowicz L (2) Feng F (3) Warta R (4) Heinicke F (5) Wšllner A (6) Briesch H (7) Sor F (8) Soller J (9) Lotsch C (10) Wong JKL (11) Park CG (12) Aubert N (13) Scheer M (14) Hu Z (15) Knoll M (16) Schlegel J (17) Frank L (18) Dao Trong P (19) Jungwirth G (20) Nohman AI (21) Maa§ K (22) Grabe N (23) Barthel M (24) Butz S (25) Lulay C (26) Zucknick M (27) Bortolomeazzi M (28) Sant P (29) Thewes V (30) Michel L (31) Fremd C (32) Schneeweiss A (33) Sun C (34) Rippe K (35) Abdollahi A (36) von Deimling A (37) Unterberg A (38) Krieg SM (39) Mallm JP (40) Lichter P (41) Zapatka M (42) Seiffert M (43) Herold-Mende C

Cancer cell

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.

Citation: Cancer Cell 2026 Jun 8 44:1290-1311.e16 Epub04/27/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42049023

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

Featured  

(1) Serger C (2) Rebuffet L (3) Sandholzer MT (4) Rackwitz W (5) Fusi I (6) Herzig P (7) BrŸggemann A (8) Pawlow CA (9) Chichelnitskiy E (10) Hajnal D (11) Oelgarth N (12) Uzun S (13) Schulthei§ C (14) Zingg A (15) Tundo S (16) Luu TT (17) Hojski A (18) Lardinois D (19) Neubert L (20) Binder M (21) Mertz K (22) Trefny MP (23) Kirchhammer N (24) Natoli M (25) Matter MS (26) LŠubli H (27) Falk C (28) Schaeuble K (29) Vivier E (30) Romagnani A (31) Zippelius A

Science immunology

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.

(1) Serger C (2) Rebuffet L (3) Sandholzer MT (4) Rackwitz W (5) Fusi I (6) Herzig P (7) BrŸggemann A (8) Pawlow CA (9) Chichelnitskiy E (10) Hajnal D (11) Oelgarth N (12) Uzun S (13) Schulthei§ C (14) Zingg A (15) Tundo S (16) Luu TT (17) Hojski A (18) Lardinois D (19) Neubert L (20) Binder M (21) Mertz K (22) Trefny MP (23) Kirchhammer N (24) Natoli M (25) Matter MS (26) LŠubli H (27) Falk C (28) Schaeuble K (29) Vivier E (30) Romagnani A (31) Zippelius A

Science immunology

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.

Citation: Sci Immunol 2026 Jun 5 11:eaeb6645 Epub06/05/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42247486

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

(1) Caro AA (2) Kancheva D (3) Hadadi E (4) Boeckx B (5) De Nolf C (6) Bardet PMR (7) Verstaen K (8) Li L (9) Oueslati L (10) Figueras-Duch N (11) Elkrim Y (12) Vandamme N (13) Deschoemaeker S (14) Blomme A (15) Close P (16) Janssens S (17) De Palma M (18) Lambrechts D (19) Coosemans A (20) Laoui D

Nature communications

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

(1) Caro AA (2) Kancheva D (3) Hadadi E (4) Boeckx B (5) De Nolf C (6) Bardet PMR (7) Verstaen K (8) Li L (9) Oueslati L (10) Figueras-Duch N (11) Elkrim Y (12) Vandamme N (13) Deschoemaeker S (14) Blomme A (15) Close P (16) Janssens S (17) De Palma M (18) Lambrechts D (19) Coosemans A (20) Laoui D

Nature communications

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.

Citation: Nat Commun 2026 May 30 Epub05/30/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42218150

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

(1) Ghannam JY (2) Bryan J (3) Weiss J (4) Kovarsky D (5) Merrell D (6) Messer C (7) Schlueter-Kuck K (8) Fell GG (9) Lim-Fat MJ (10) Youssef G (11) Rahman R (12) Qin L (13) Young GS (14) Janes JT 3rd (15) Van Orden M (16) Pfaff KL (17) Gans A (18) Lin ES (19) Huang RY (20) Danysh BP (21) Parida L (22) Li S (23) Wen PY (24) Chiocca EA (25) Neuberg D (26) Ligon KL (27) Tirosh I (28) Reardon DA (29) Getz G (30) Wu CJ

Nature cancer

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

Contributed by Shishir Pant

(1) Ghannam JY (2) Bryan J (3) Weiss J (4) Kovarsky D (5) Merrell D (6) Messer C (7) Schlueter-Kuck K (8) Fell GG (9) Lim-Fat MJ (10) Youssef G (11) Rahman R (12) Qin L (13) Young GS (14) Janes JT 3rd (15) Van Orden M (16) Pfaff KL (17) Gans A (18) Lin ES (19) Huang RY (20) Danysh BP (21) Parida L (22) Li S (23) Wen PY (24) Chiocca EA (25) Neuberg D (26) Ligon KL (27) Tirosh I (28) Reardon DA (29) Getz G (30) Wu CJ

Nature cancer

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

Contributed by Shishir Pant

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

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

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

Citation: Nat Cancer 2026 Jun 3 Epub06/03/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42237038

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

(1) Azimzade Y (2) Haugen MH (3) Kristensen VN (4) Frigessi A (5) Köhn-Luque A

Nature communications

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

(1) Azimzade Y (2) Haugen MH (3) Kristensen VN (4) Frigessi A (5) Köhn-Luque A

Nature communications

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.

Citation: Nat Commun 2026 May 25 Epub05/25/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42185288

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