Journal Articles

The efficacy of immunotherapy in glioma requires distal B cell responses in tumor-draining lymph nodes Spotlight 

(1) Kim Y (2) Kang I (3) Kang BH (4) Park WH (5) Kim CW (6) Kim HJ (7) La J (8) Kwon MS (9) Park SH (10) Im SH (11) Kim HC (12) Ku KB (13) Kim M (14) Oh JE (15) Lee HK

Science immunology

Kim et al. showed that anti-CTLA-4 efficacy in orthotopic murine glioblastoma (GBM) models was abolished by B cell deficiency and tdLN removal. In WT mice, anti-CTLA-4 increased B cell numbers and GC reactions in tdLNs, but not in the TIME, and therapeutic efficacy required CD4+ T cell help to B cells. While maintaining tumor-reactive CD4+ T cell function in the TIME, in tdLNs, anti-CTLA-4 therapy boosted TFH cell differentiation to support GC B cell clonal expansion and generation of antibody-secreting cells producing tumor-binding class-switched IgG1 antibodies that promoted macrophage-mediated phagocytosis of glioma cells via Fc receptor signaling.

Contributed by Paula Hochman

(1) Kim Y (2) Kang I (3) Kang BH (4) Park WH (5) Kim CW (6) Kim HJ (7) La J (8) Kwon MS (9) Park SH (10) Im SH (11) Kim HC (12) Ku KB (13) Kim M (14) Oh JE (15) Lee HK

Science immunology

Kim et al. showed that anti-CTLA-4 efficacy in orthotopic murine glioblastoma (GBM) models was abolished by B cell deficiency and tdLN removal. In WT mice, anti-CTLA-4 increased B cell numbers and GC reactions in tdLNs, but not in the TIME, and therapeutic efficacy required CD4+ T cell help to B cells. While maintaining tumor-reactive CD4+ T cell function in the TIME, in tdLNs, anti-CTLA-4 therapy boosted TFH cell differentiation to support GC B cell clonal expansion and generation of antibody-secreting cells producing tumor-binding class-switched IgG1 antibodies that promoted macrophage-mediated phagocytosis of glioma cells via Fc receptor signaling.

Contributed by Paula Hochman

ABSTRACT: Humoral immunity, mediated by B cells that mature in germinal centers in lymph nodes (LNs), is essential for adaptive immune responses, but its role in antitumor immunity and responses to immunotherapy remain unclear. Here, we show that activation of B cells in tumor-draining deep cervical LNs (dcLNs) is necessary for the efficacy of CTLA-4 (cytotoxic T lymphocyte-associated protein 4) immune checkpoint blockade in glioma in vivo. Anti-CTLA-4 therapy enhanced T follicular helper cell (T(FH) cell) expansion in dcLNs, leading to germinal center B cell responses, immunoglobulin G (IgG) class switching, and the generation of glioma-reactive antibodies. Glioma-bearing mice lacking antibody-secreting cells did not benefit from CTLA-4 blockade. Distally secreted IgG accumulated in the tumor microenvironment and promoted glioma cell phagocytosis in vivo. These findings define a B cell-dependent mechanism underlying CTLA-4-mediated control of glioma and provide a conceptual framework for future therapeutic strategies in tumor.

Author Info: (1) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (2) Laboratory of Host D

Author Info: (1) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (2) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (3) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (4) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (5) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (6) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (7) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (8) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (9) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (10) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (11) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (12) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Department of Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea. (13) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (14) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (15) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Regenerative Medical Research Institute (reMRI) for Aging-Related Diseases, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Citation: Sci Immunol 2026 Jul 10 11:eadz2494 Epub07/10/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42430444

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Generalizable AI predicts immunotherapy outcomes across cancers and treatments Spotlight 

(1) Shen W (2) Moon I (3) Nguyen TH (4) Li MM (5) Huang Y (6) Nair N (7) Marbach D (8) Zitnik M

Nature medicine

Shen et al. developed COMPASS, an AI model that interprets pre-treatment tumor RNAseq data in the context of tumor-immune gene expression modules and maps them to predict ICB response. Trained on TCGA and cohort data, COMPASS was applicable across cohorts, indications, ICB drugs, and targets, with superior response prediction compared to established models or correlates (TMB, PD-L1). COMPASS also generated personalized “response maps” identifying potential resistance mechanisms; unexpected nonresponders (i.e. patients with an inflammatory TME) often had gene expression associated with angiogenesis, TGFβ, and B cell deficiency.

Contributed by Alex Najibi

(1) Shen W (2) Moon I (3) Nguyen TH (4) Li MM (5) Huang Y (6) Nair N (7) Marbach D (8) Zitnik M

Nature medicine

Shen et al. developed COMPASS, an AI model that interprets pre-treatment tumor RNAseq data in the context of tumor-immune gene expression modules and maps them to predict ICB response. Trained on TCGA and cohort data, COMPASS was applicable across cohorts, indications, ICB drugs, and targets, with superior response prediction compared to established models or correlates (TMB, PD-L1). COMPASS also generated personalized “response maps” identifying potential resistance mechanisms; unexpected nonresponders (i.e. patients with an inflammatory TME) often had gene expression associated with angiogenesis, TGFβ, and B cell deficiency.

Contributed by Alex Najibi

ABSTRACT: Immune checkpoint inhibitors (ICIs) are a standard treatment across cancers, yet most patients do not respond, and existing biomarkers generalize poorly across tumor types and therapies. Here we present COMPASS, a pan-cancer foundation model that predicts immunotherapy response from bulk tumor transcriptomes using a concept bottleneck transformer. COMPASS encodes gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interaction and signaling pathways. Trained on 10,184 tumors across 33 cancer types, COMPASS achieves better average performance than 22 methods across 16 clinical cohorts spanning seven cancers and six ICIs, improving accuracy by 8.5% and area under the precision-recall curve by 15.7% on average across cohorts. COMPASS generalizes to cancer types and treatments not represented during fine-tuning and may inform indication selection and patient stratification. In survival analyses, patients classified by COMPASS as responders had longer overall survival (hazard ratio_=_4.7, P_<_0.0001). Personalized response maps connect gene expression to immune concepts, identifying programs associated with response and resistance; in immune-inflamed non-responders, COMPASS highlights programs including TGF_ signaling, endothelial exclusion, CD4(+) T cell dysfunction and B cell deficiency. COMPASS predicts immunotherapy response and provides hypothesis-generating mechanistic insight for trial design and translational studies.

Author Info: (1) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. (2) Department of Biome

Author Info: (1) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. (2) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (3) Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. (4) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (5) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (6) Roche Pharma Research and Early Development, Oncology Early Clinical Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland. (7) Computational Sciences Center of Excellence, F. Hoffmann-La Roche Ltd., Basel, Switzerland. daniel.marbach.dm1@roche.com. (8) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. marinka@hms.harvard.edu. Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA. marinka@hms.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. marinka@hms.harvard.edu. Harvard Data Science Initiative, Cambridge, MA, USA. marinka@hms.harvard.edu.

Citation: Nat Med 2026 Jul 3 Epub07/03/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42399673

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Addressing Biases in Analysis of Time of Infusion: NCI/SWOG Trial S1404 Among Participants With High-Risk Resectable Melanoma Who Received Adjuvant Anti-PD-1 Therapy Spotlight 

(1) Othus M (2) Truong TG (3) Sharon E (4) Kendra K (5) Grossmann K (6) Buchbinder E (7) Khushalani NI (8) Eroglu Z (9) Chandra S (10) Doolittle GC (11) Kirkwood JM (12) Ikeguchi A (13) Mihalcioiu C (14) Cowey CL (15) Reddy SA (16) Johnson DB (17) Taylor M (18) Sondak VK (19) Ribas A (20) Patel SP

JCO oncology practice | Free PMC Article

In an analysis of a multi-center trial involving 628 patients with high-risk melanoma receiving adjuvant pembrolizumab, Othus et al. identified optimal time cut-points for the first infusion of 1:18 pm for recurrence-free survival and 3:48 pm for overall survival (OS). These findings, however, did not reach statistical significance regarding patient outcomes. Furthermore, the lack of threshold robustness was demonstrated when shifting the OS cut-point 30 minutes earlier, which yielded a hazard ratio of 0.98. Average infusion times trended earlier over the year, while appointments were on average later for patients living further from the treatment center.

Contributed by Ute Burkhardt

(1) Othus M (2) Truong TG (3) Sharon E (4) Kendra K (5) Grossmann K (6) Buchbinder E (7) Khushalani NI (8) Eroglu Z (9) Chandra S (10) Doolittle GC (11) Kirkwood JM (12) Ikeguchi A (13) Mihalcioiu C (14) Cowey CL (15) Reddy SA (16) Johnson DB (17) Taylor M (18) Sondak VK (19) Ribas A (20) Patel SP

JCO oncology practice | Free PMC Article

In an analysis of a multi-center trial involving 628 patients with high-risk melanoma receiving adjuvant pembrolizumab, Othus et al. identified optimal time cut-points for the first infusion of 1:18 pm for recurrence-free survival and 3:48 pm for overall survival (OS). These findings, however, did not reach statistical significance regarding patient outcomes. Furthermore, the lack of threshold robustness was demonstrated when shifting the OS cut-point 30 minutes earlier, which yielded a hazard ratio of 0.98. Average infusion times trended earlier over the year, while appointments were on average later for patients living further from the treatment center.

Contributed by Ute Burkhardt

PURPOSE: Multiple reports have suggested that receiving immunotherapy infusions earlier in the day is associated with improved outcomes, including longer overall survival (OS) and lower toxicity rates. However, the definition of early varies between publications. Reports also fail to account for confounding factors (including distance to infusion center), are subject to survivor bias (analyzing postbaseline factors at baseline), and do not adjust P values for multiple comparisons when evaluating multiple potential thresholds for early versus late time of day of infusion. METHODS: We analyzed a previously reported multicenter clinical trial evaluating pembrolizumab as adjuvant therapy for participants with resectable high-risk melanoma. Standard statistical methodologies that account for potential biasses were used to evaluate the association between time of day of infusion and clinical outcomes. RESULTS: A total of 628 participants received pembrolizumab and had time of first infusion recorded. The median age was 55 years, range, 20-82. Odds of infusion before 11:00 hours increased by 32% over 12 months of therapy (P = .013). Participants living further from their treating institution had later infusion times on average: odds of infusion before 11:00 decreased by 9% for each additional 50 miles (P = .017). The optimal cut point for first infusion time for OS was 15:48 with hazard ratio (HR) = 1.40; changing the cut point by 30 minutes earlier to 15:18 decreased HR to 0.98, indicating lack of robustness of the threshold. No significant association was identified between proportion of early infusions and outcomes in multivariable time-dependent Cox regression models. CONCLUSION: In this multicenter trial of adjuvant pembrolizumab for participants with high-risk melanoma, analyses that account for common sources of bias found no significant association between recurrence-free or OS and time of day of infusion.

Author Info: (1) Division of Public Health, Fred Hutchinson Cancer Center, Seattle WA. (2) Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH. (3)

Author Info: (1) Division of Public Health, Fred Hutchinson Cancer Center, Seattle WA. (2) Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH. (3) Department of Medicine, Dana Farber Cancer Institute, Boston, MA. Harvard Medical School, Boston, MA. (4) Medical Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH. (5) Medical Oncology, Providence Cancer Institute, Portland, OR. (6) Medical Oncology, Mass General Brigham Cancer Institute, Boston, MA. (7) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (8) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (9) Division of Hematology and Oncology, Robert H Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL. (10) Division of Clinical Oncology, University of Kansas Medical Center, Kansas City, KS. (11) Melanoma Program, University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA. (12) Melanoma Medical Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX. (13) Medical Oncology, McGill University Health Centre, Montreal, Canada. (14) Melanoma, Texas Oncology-Baylor Sammons Cancer Center, Dallas, TX. (15) Department of Medical Oncology, Stanford University School of Medicine, Palo Alto, CA. (16) Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. (17) Medical Oncology, Providence Cancer Institute, Portland, OR. (18) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (19) Department of Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA. (20) Department of Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO.

Citation: JCO Oncol Pract 2026 Jun 23 OP2501413 Epub06/23/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42335437

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Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling Spotlight 

(1) Suissa D (2) Fidelle M (3) Reich E (4) Pham TN (5) Thomas S (6) Bjšrk JR (7) Liu P (8) Zhao L (9) Kitaoka K (10) Piard E (11) Lebhar I (12) Tian AL (13) Thelemaque C (14) Alves Costa Silva C (15) Deutsch E (16) Loriot Y (17) Segata N (18) Piccinno G (19) Hospers GAP (20) Maleki Vareki S (21) Silverman MS (22) Lenehan JG (23) Bataille V (24) Boulate D (25) Kuznetsova T (26) Weersma RK (27) Messaoudene M (28) Durand S (29) van der Aalst CM (30) de Koning HJ (31) Schuler-Thurner B (32) de Vries IJM (33) Rafie E (34) Saliby RM (35) Machaalani M (36) Haferkamp S (37) Schilling B (38) Porcari S (39) Ciccarese C (40) Iacovelli R (41) Cremolini C (42) Choueiri TK (43) Elkrief A (44) Kroemer G (45) Heinzerling L (46) Chamoto K (47) Ianiro G (48) Routy B (49) Derosa L (50) Paragios N (51) Zitvogel L

Nature medicine

Suissa and Fidelle et al. performed targeted metabolomics to 4,336 plasma samples from 1,714 ICI-treated patients across 16 cohorts and trained an ML model that predicted 12‑month PFS, with histidine as a favorable marker and long-chain fatty acids and succinate associated with poor outcome. Histidine supplementation promoted mitochondrial FAO and regulated T cell exhaustion, enhancing ICI-induced antitumor immunity in fibrosarcoma and melanoma models. Histidine-rich diet was associated with favorable PFS in patients without dysbiosis-associated histidine catabolism, and fecal histidine levels inversely correlated with severe irAEs.

Contributed by Shishir Pant

(1) Suissa D (2) Fidelle M (3) Reich E (4) Pham TN (5) Thomas S (6) Bjšrk JR (7) Liu P (8) Zhao L (9) Kitaoka K (10) Piard E (11) Lebhar I (12) Tian AL (13) Thelemaque C (14) Alves Costa Silva C (15) Deutsch E (16) Loriot Y (17) Segata N (18) Piccinno G (19) Hospers GAP (20) Maleki Vareki S (21) Silverman MS (22) Lenehan JG (23) Bataille V (24) Boulate D (25) Kuznetsova T (26) Weersma RK (27) Messaoudene M (28) Durand S (29) van der Aalst CM (30) de Koning HJ (31) Schuler-Thurner B (32) de Vries IJM (33) Rafie E (34) Saliby RM (35) Machaalani M (36) Haferkamp S (37) Schilling B (38) Porcari S (39) Ciccarese C (40) Iacovelli R (41) Cremolini C (42) Choueiri TK (43) Elkrief A (44) Kroemer G (45) Heinzerling L (46) Chamoto K (47) Ianiro G (48) Routy B (49) Derosa L (50) Paragios N (51) Zitvogel L

Nature medicine

Suissa and Fidelle et al. performed targeted metabolomics to 4,336 plasma samples from 1,714 ICI-treated patients across 16 cohorts and trained an ML model that predicted 12‑month PFS, with histidine as a favorable marker and long-chain fatty acids and succinate associated with poor outcome. Histidine supplementation promoted mitochondrial FAO and regulated T cell exhaustion, enhancing ICI-induced antitumor immunity in fibrosarcoma and melanoma models. Histidine-rich diet was associated with favorable PFS in patients without dysbiosis-associated histidine catabolism, and fecal histidine levels inversely correlated with severe irAEs.

Contributed by Shishir Pant

ABSTRACT: Immune-checkpoint inhibitors benefit a subset of patients with advanced cancer, and the metabolic determinants of response remain unclear. Here, using targeted metabolomics and metagenomics, we profiled 4,336 plasma samples from 1,714 patients across five tumor types and 16 cohorts spanning Europe and North America, longitudinally sampled during five immune-checkpoint inhibitor-based treatment modalities, including fecal microbiota transplantation. A multimodal machine-learning framework integrating 154 metabolites with clinical variables identified five metabolites, age, body mass index and renal function as predictors of 12-month progression-free survival. The model achieved areas under the curve of 0.88 in training and 0.73 in validation cohorts of 105 and 30 patients, respectively and generalized across seven external cohorts. Histidine was a favorable prognostic feature of survival, whereas long-chain fatty acids and succinate were negatively associated with outcome. Histidine supplementation enhanced antitumor immunity in mice. Histidine-rich diets improved progression-free survival in patients lacking dysbiotic microbiome signatures associated with histidine catabolism.

Author Info: (1) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (2) UniversitŽ Paris-Saclay,

Author Info: (1) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (2) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (3) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (4) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (5) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (6) Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (7) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (8) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (9) Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. (10) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (11) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (12) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (13) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (14) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Oncoclinicas&Co - Medica Scientia Innovation Research (MEDSIR), Sao Paulo, Brazil. Gonalo Moniz Institute, Fiocruz, Salvador, Brazil. Federal University of Bahia, Salvador, Brazil. (15) Department of Radiation Oncology, Gustave Roussy, UniversitŽ Paris-Saclay, INSERM U1355, RHU LySAIRI, Villejuif, France. (16) Department of Therapeutic Innovation and Early Trials (DITEP), INSERM U981, Gustave Roussy, Villejuif, France. (17) Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy. (18) Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy. (19) Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (20) Verspeeten Family Cancer Centre, London Health Sciences Research Institute, London, Ontario, Canada. Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada. Department of Oncology, Division of Experimental Oncology, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. (21) Lawson Health Research Institute, London, Ontario, Canada. Department of Microbiology & Immunology, Western University, London, Ontario, Canada. Department of Medicine, Division of Infectious Diseases, Western University, London, Ontario, Canada. Division of Infectious Diseases, St Joseph's Health Care, London, Ontario, Canada. (22) Verspeeten Family Cancer Centre, London Health Sciences Research Institute, London, Ontario, Canada. Department of Oncology, Western University, London, Ontario, Canada. (23) Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. Department of Dermatology, Mount Vernon Cancer Centre, Northwood, UK. Department of Dermatology, Hemel Hempstead Hospital, West Hertfordshire NHS Trust, Hemel Hempstead, UK. (24) Department of Thoracic Surgery, H™pital-Nord-APHM, Aix-Marseille University, Marseille, France. H™pital Marie Lannelongue, GHPSJ, Le Plessis-Robinson, France. (25) Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium. (26) Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (27) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. (28) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (29) Department of Public Health, Erasmus Medical Centre - University Medical Centre Rotterdam, Rotterdam, The Netherlands. (30) Department of Public Health, Erasmus Medical Centre - University Medical Centre Rotterdam, Rotterdam, The Netherlands. (31) Department of Dermatology, Friedrich-Alexander-UniversitŠt Erlangen-NŸrnberg (FAU), UniversitŠtsklinikum Erlangen, Erlangen, Germany. Bavarian Cancer Research Center (BZKF), Erlangen, Germany. (32) Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands. (33) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (34) Dana-Farber Cancer Institute, Boston, MA, USA. Yale School of Medicine, New Haven, CT, USA. (35) Dana-Farber Cancer Institute, Boston, MA, USA. (36) Bavarian Cancer Research Center (BZKF), Erlangen, Germany. Department of Dermatology, University Hospital Regensburg, Regensburg, Germany. (37) Department of Dermatology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany. (38) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC CEMAD Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli IRCCS, Rome, Italy. (39) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC Oncologia Medica, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. (40) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC Oncologia Medica, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. (41) Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy. (42) Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (43) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (44) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. Institut du Cancer Paris CARPEM, Department of Biology, H™pital EuropŽen Georges Pompidou, AP-HP, Paris, France. (45) Department of Dermatology, Friedrich-Alexander-UniversitŠt Erlangen-NŸrnberg (FAU), UniversitŠtsklinikum Erlangen, Erlangen, Germany. Bavarian Cancer Research Center (BZKF), Erlangen, Germany. Department of Dermatology and Allergy, LMU University Hospital LMU Munich, Munich, Germany. (46) Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. Division of Cancer Immune Regulation, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. (47) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC CEMAD Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli IRCCS, Rome, Italy. (48) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (49) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Department of Medical Oncology, Gustave Roussy, Villejuif, France. (50) MICS Laboratory, CentraleSupŽlec, UniversitŽ Paris-Saclay, Gif-sur-Yvette, France. nikos.paragios@centralesupelec.fr. TheraPanacea, Paris, France. nikos.paragios@centralesupelec.fr. (51) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Laurence.zitvogel@gustaveroussy.fr.

Citation: Nat Med 2026 Jun 25 Epub06/25/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42350644

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Tumor suppressor genotype influences the extent and mode of immunosurveillance in lung cancer Spotlight 

(1) Adler KM (2) Xu H (3) Gladstein AC (4) Irizarry-Negron VM (5) Robertson MR (6) Doerig KR (7) Petrov DA (8) Winslow MM (9) Feldser DM

Nature communications

Using genetically engineered conditional mouse models and lentiviral-mediated somatic gene inactivation, Adler and Xu et al. developed models that allowed them to quantify immunoediting by evaluating fixed neoantigen expression against genotypic tumor backgrounds defined by common driver mutations and different tumor suppressor genes. While genetic features promoting tumor proliferation generally correlated with increased sensitivity to immunosurveillance, different genotypes differentially affected immune cell recruitment, selection of tumor cells with neoantigen silencing, tumor growth, and mechanisms of immune evasion.

Contributed by Lauren Hitchings

(1) Adler KM (2) Xu H (3) Gladstein AC (4) Irizarry-Negron VM (5) Robertson MR (6) Doerig KR (7) Petrov DA (8) Winslow MM (9) Feldser DM

Nature communications

Using genetically engineered conditional mouse models and lentiviral-mediated somatic gene inactivation, Adler and Xu et al. developed models that allowed them to quantify immunoediting by evaluating fixed neoantigen expression against genotypic tumor backgrounds defined by common driver mutations and different tumor suppressor genes. While genetic features promoting tumor proliferation generally correlated with increased sensitivity to immunosurveillance, different genotypes differentially affected immune cell recruitment, selection of tumor cells with neoantigen silencing, tumor growth, and mechanisms of immune evasion.

Contributed by Lauren Hitchings

ABSTRACT: The impact of cancer driving mutations on immunosurveillance throughout tumor development remains poorly understood. To better understand the contribution of tumor genotype to immunosurveillance, we generated and validated lentiviral-based vectors that create increasingly immunogenic neoantigens. This vector system is compatible with autochthonous Cre-regulated cancer models, CRISPR/Cas9-mediated somatic genome editing, and tumor barcoding. Here, we show that in the context of oncogenic KRAS-driven lung cancer and strong neoantigen expression, tumor suppressor genotype dictates the degree of immune cell recruitment, positive selection of tumors with neoantigen silencing, and tumor outgrowth. By quantifying the impact of 11 commonly inactivated tumor suppressor genes on tumor growth across neoantigenic contexts, we show that the growth-promoting effects of tumor suppressor gene inactivation correlate with increasing sensitivity to immunosurveillance. Importantly, some genotypes also dramatically changed sensitivity to immunosurveillance independently of their growth-promoting effects. We propose a model of immunoediting in which tumor suppressor gene inactivation works in tandem with neoantigen expression to shape tumor immunosurveillance and immunoediting such that the same neoantigens uniquely modulate tumor immunoediting depending on the genetic context.

Author Info: (1) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medi

Author Info: (1) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (2) Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. (3) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (4) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (5) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (6) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (7) Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. (8) Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. (9) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu.

Citation: Nat Commun 2026 Jun 15 Epub06/15/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42297823

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CD4+ T cells impair tumor growth through IL-3 and TNF-dependent vascular damage

Spotlight 

(1) Lian Q (2) Nie J (3) Singh J (4) Chen Q (5) Matta J (6) Chan W (7) Balmaceno-Criss M (8) Vacchio MS (9) Yu W (10) Clark AD (11) Edmondson E (12) Kelly MC (13) Germain RN (14) Bosselut R

Science

Lian and Nie et al. showed that LCMV gp66-specific CD4+ T cells inhibited tumor growth in an MC38-GP model in an antigen-specific manner, independent of direct lymphoid cell-mediated cytotoxicity. CD4+ T cells initiated antigen-dependent perivascular, myeloid cell-dense structures in the TIME, reprogrammed myeloid transcriptomes, and leveraged recruited myeloid cells to control tumor growth. Single-cell and spatial transcriptomics showed that CD4+ T cell-derived IL-3 programmed macrophages to secrete tumor necrosis factor, which damaged intratumoral vasculature, compromised blood supply, and induced localized tumor cell death and regression.

Contributed by Shishir Pant

(1) Lian Q (2) Nie J (3) Singh J (4) Chen Q (5) Matta J (6) Chan W (7) Balmaceno-Criss M (8) Vacchio MS (9) Yu W (10) Clark AD (11) Edmondson E (12) Kelly MC (13) Germain RN (14) Bosselut R

Science

Lian and Nie et al. showed that LCMV gp66-specific CD4+ T cells inhibited tumor growth in an MC38-GP model in an antigen-specific manner, independent of direct lymphoid cell-mediated cytotoxicity. CD4+ T cells initiated antigen-dependent perivascular, myeloid cell-dense structures in the TIME, reprogrammed myeloid transcriptomes, and leveraged recruited myeloid cells to control tumor growth. Single-cell and spatial transcriptomics showed that CD4+ T cell-derived IL-3 programmed macrophages to secrete tumor necrosis factor, which damaged intratumoral vasculature, compromised blood supply, and induced localized tumor cell death and regression.

Contributed by Shishir Pant

ABSTRACT: Most cancer immunotherapy strategies are focused on direct tumor killing by immune cells, especially T lymphocytes. Clinical and conceptual limitations of these approaches create a need for additional strategies. We identified a tumor stroma-targeting mechanism in which tumor antigen-specific CD4(+) T cells inhibit tumor growth through myeloid cell and tumor necrosis factor (TNF)-dependent vascular damage. Multiplex immunofluorescence and single-cell and tissue transcriptomics showed that CD4(+) T cells trigger the formation of perivascular myeloid cell clusters containing "classically activated" macrophages that produce TNF in response to T cell-derived interleukin-3. TNF causes intratumoral endothelial damage and blood supply disruption, which are associated with localized tumor cell death. Thus, intratumoral antigen-triggered T cell activation can mediate antitumor effects without direct recognition of living tumor cells, thereby avoiding many of the inhibitory mechanisms that limit anti-tumor immunity.

Author Info: (1) Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. (2) La

Author Info: (1) Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. (2) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (3) Single Cell Analysis Facility, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (4) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (5) Molecular Histopathology Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. (6) Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. (7) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (8) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (9) Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. (10) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (11) Molecular Histopathology Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. (12) Single Cell Analysis Facility, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. (13) Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. (14) Laboratory of Immune Cell Biology and Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Citation: Science 2026 Jun 18 392:eads7910 Epub06/18/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42313973

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

(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

Tags:

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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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

Tags:

Targeting CCR1 remodels the tumor microenvironment and relieves immune suppression in pancreatic cancer Featured  

(1) Zhang Y (2) Kadiyala P (3) Yan W (4) Brown K (5) Avritt FR (6) Donahue KL (7) Procario MC (8) Okoye JO (9) Giridharan T (10) Elhossiny AM (11) Espinoza CE (12) Awad D (13) Lasse Opsahl EL (14) Medina-Cabrera PI (15) Velez-Delgado A (16) Menjivar RE (17) Yang OA (18) Yang S (19) He X (20) Gupta S (21) Tariq R (22) Brandt AR (23) Wang X (24) denDekker A (25) Nwosu ZC (26) Carpenter ES (27) Courtney AH (28) Bednar F (29) Frankel TL (30) Lyssiotis CA (31) Zheng B (32) Kryczek I (33) Pasca di Magliano M

Cancer immunology research

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.

(1) Zhang Y (2) Kadiyala P (3) Yan W (4) Brown K (5) Avritt FR (6) Donahue KL (7) Procario MC (8) Okoye JO (9) Giridharan T (10) Elhossiny AM (11) Espinoza CE (12) Awad D (13) Lasse Opsahl EL (14) Medina-Cabrera PI (15) Velez-Delgado A (16) Menjivar RE (17) Yang OA (18) Yang S (19) He X (20) Gupta S (21) Tariq R (22) Brandt AR (23) Wang X (24) denDekker A (25) Nwosu ZC (26) Carpenter ES (27) Courtney AH (28) Bednar F (29) Frankel TL (30) Lyssiotis CA (31) Zheng B (32) Kryczek I (33) Pasca di Magliano M

Cancer immunology research

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

Citation: Cancer Immunol Res 2026 May 28 Epub05/28/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42207977

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