Journal Articles

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

(1) Nguyen TT (2) Gómez H (3) Lutge M (4) Yángüez E (5) Hüsser T (6) Nassiri S (7) Trumpfheller C (8) Colombetti S (9) Codarri Deak L (10) Umaña P (11) Tugues S (12) Grazina de Matos I (13) Kunz L

Journal for immunotherapy of cancer | Free PMC Article

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

Contributed by Alex Najibi

(1) Nguyen TT (2) Gómez H (3) Lutge M (4) Yángüez E (5) Hüsser T (6) Nassiri S (7) Trumpfheller C (8) Colombetti S (9) Codarri Deak L (10) Umaña P (11) Tugues S (12) Grazina de Matos I (13) Kunz L

Journal for immunotherapy of cancer | Free PMC Article

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

Contributed by Alex Najibi

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

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

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

Citation: J Immunother Cancer 2026 May 28 14: Epub05/28/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42208978

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

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

Nature communications

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

Nature communications

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

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

(1) Zhu L (2) Zhang S (3) Li B (4) Liu X (5) Yang C (6) Tang X (7) Chai J (8) Yang X (9) Yu C (10) Liao H (11) Cao Z (12) Liao L (13) Wang W (14) Yuan S (15) Gao Q (16) Sun C (17) Bernards R (18) Yang Y (19) Qin W (20) Wang C

Cancer cell

(1) Zhu L (2) Zhang S (3) Li B (4) Liu X (5) Yang C (6) Tang X (7) Chai J (8) Yang X (9) Yu C (10) Liao H (11) Cao Z (12) Liao L (13) Wang W (14) Yuan S (15) Gao Q (16) Sun C (17) Bernards R (18) Yang Y (19) Qin W (20) Wang C

Cancer cell

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

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

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

Citation: Cancer Cell 2026 May 28 Epub05/28/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42208540

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Pan-cancer spatial atlas of tertiary lymphoid structures

(1) Cho KS (2) Liu Y (3) Pei G (4) Chen J (5) Dai Y (6) Liu Y (7) Zhou T (8) Bougouin A (9) Serrano A (10) Wani K (11) Jadhav A (12) Min J (13) Hernandez S (14) Lu W (15) Zhang D (16) Jiang J (17) Shamsutdinova D (18) Dai E (19) Peng F (20) Sinjab A (21) Guerrero PA (22) Julio ICL (23) Yu K (24) Clark H (25) Maru D (26) Li M (27) Futreal A (28) Lee S (29) Solis Soto LM (30) Shang L (31) Msaouel P (32) Ajani JA (33) Beird H (34) Jazaeri AA (35) Lazar AJ (36) Sautes-Fridman C (37) Fridman WH (38) Maitra A (39) Kadara H (40) Gao J (41) Sharma P (42) Wang L

Science

(1) Cho KS (2) Liu Y (3) Pei G (4) Chen J (5) Dai Y (6) Liu Y (7) Zhou T (8) Bougouin A (9) Serrano A (10) Wani K (11) Jadhav A (12) Min J (13) Hernandez S (14) Lu W (15) Zhang D (16) Jiang J (17) Shamsutdinova D (18) Dai E (19) Peng F (20) Sinjab A (21) Guerrero PA (22) Julio ICL (23) Yu K (24) Clark H (25) Maru D (26) Li M (27) Futreal A (28) Lee S (29) Solis Soto LM (30) Shang L (31) Msaouel P (32) Ajani JA (33) Beird H (34) Jazaeri AA (35) Lazar AJ (36) Sautes-Fridman C (37) Fridman WH (38) Maitra A (39) Kadara H (40) Gao J (41) Sharma P (42) Wang L

Science

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

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

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

Citation: Science 2026 May 28 392:eadz2742 Epub05/28/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42207882

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

(1) Sun R (2) Liu S (3) Yang X (4) Che C (5) Zhang Z (6) Zhang C (7) Wang Y (8) Yang Y (9) Li X (10) Wang J (11) Zheng H (12) Guo M (13) Yin H

Cancer immunology research

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

Contributed by Katherine Turner

(1) Sun R (2) Liu S (3) Yang X (4) Che C (5) Zhang Z (6) Zhang C (7) Wang Y (8) Yang Y (9) Li X (10) Wang J (11) Zheng H (12) Guo M (13) Yin H

Cancer immunology research

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

Contributed by Katherine Turner

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

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

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

Citation: Cancer Immunol Res 2026 May 15 Epub05/15/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42139378

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

(1) Navarre S (2) Ishibashi MN (3) Nair A (4) Reyes-Torres I (5) Belabed M (6) Halasz L (7) Park MD (8) Mattiuz R (9) Ounadjela M (10) Gunset G (11) Mansilla-Soto J (12) Feucht J (13) Cabriolu A (14) Le Berichel J (15) Birbrair A (16) Eyquem J (17) Brown BD (18) Merad M (19) Sadelain M (20) Ahmed J

Nature cancer

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

Contributed by Shishir Pant

(1) Navarre S (2) Ishibashi MN (3) Nair A (4) Reyes-Torres I (5) Belabed M (6) Halasz L (7) Park MD (8) Mattiuz R (9) Ounadjela M (10) Gunset G (11) Mansilla-Soto J (12) Feucht J (13) Cabriolu A (14) Le Berichel J (15) Birbrair A (16) Eyquem J (17) Brown BD (18) Merad M (19) Sadelain M (20) Ahmed J

Nature cancer

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

Contributed by Shishir Pant

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

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

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

Citation: Nat Cancer 2026 May 22 Epub05/22/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42174275

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Immune-remodeling mRNAs expressing IRF8 or NIK generate durable antitumor immunity in multiple cancer models Spotlight 

(1) Gupta A (2) Das R (3) Reed K (4) Jeon T (5) Nguyen QTC (6) Rudra A (7) Ge X (8) Trongjit S (9) Vanrobaeys YS (10) Langer R (11) Weissleder R (12) Garris C (13) Anderson DG

Nature biotechnology

In mice, i.t. or i.v. delivery of CKK-E12-LNPs loaded with immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase (NIK) or IFN regulatory factor 8 (IRF8) induced (1) APC activation and maturation into cDC1s, (2) a release of immunostimulatory cytokines, (3) accumulation of NKT and γδT cells in tumors, and (4) priming of antitumor CD8+ T cells, which infiltrated and eliminated tumors and protected mice from rechallenge. In combination with mRNA encoding OVA, IR-mRNA prevented growth of OVA+ tumors. IR-mRNAs also synergized with anti-PD-1, and enhanced humoral and adaptive immune responses to infectious disease antigens.

Contributed by Lauren Hitchings

(1) Gupta A (2) Das R (3) Reed K (4) Jeon T (5) Nguyen QTC (6) Rudra A (7) Ge X (8) Trongjit S (9) Vanrobaeys YS (10) Langer R (11) Weissleder R (12) Garris C (13) Anderson DG

Nature biotechnology

In mice, i.t. or i.v. delivery of CKK-E12-LNPs loaded with immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase (NIK) or IFN regulatory factor 8 (IRF8) induced (1) APC activation and maturation into cDC1s, (2) a release of immunostimulatory cytokines, (3) accumulation of NKT and γδT cells in tumors, and (4) priming of antitumor CD8+ T cells, which infiltrated and eliminated tumors and protected mice from rechallenge. In combination with mRNA encoding OVA, IR-mRNA prevented growth of OVA+ tumors. IR-mRNAs also synergized with anti-PD-1, and enhanced humoral and adaptive immune responses to infectious disease antigens.

Contributed by Lauren Hitchings

ABSTRACT: Although immunotherapy has benefited a subset of persons with cancer, its broader efficacy remains limited, primarily because of an immunosuppressive tumor microenvironment characterized by insufficient numbers of functional tumor-specific T cells, antigen-presenting cells (APCs) and tumor-infiltrating lymphocytes. Here we engineer immune cells in the tumor microenvironment using lipid nanoparticles (LNPs) to deliver immune-remodeling mRNAs (IR-mRNAs) encoding NF-κB-inducing kinase or interferon regulatory factor 8. These IR-mRNAs activate APCs in tumors, significantly increasing activated type 1 conventional dendritic cells, immunostimulatory cytokines and priming antitumor CD8+ T cells. IR-mRNAs encapsulated in LNPs elicited durable antitumor responses in multiple syngeneic mouse tumor models through both intratumoral and intravenous delivery. Coadministration of IR-mRNA and ovalbumin mRNA elicited a ~10-fold increase in antigen-specific CD8+ T cell responses, sustained long-term memory and effectively prevented tumor growth in vaccinated mice. Additionally, coadministration of IR-mRNA and hemagglutinin mRNA enhanced the humoral response ~5-fold and the cellular response ~15-fold, underscoring their potential as adjuvants for boosting adaptive immunity.

Author Info: (1) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute

Author Info: (1) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (2) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. (3) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (4) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (5) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (6) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA. (7) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. (8) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (9) Bioinformatics & Computing Core Facility of the Swanson Biotechnology Center, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. (10) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. (11) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. Department of Systems Biology, Harvard Medical School, Boston, MA, USA. Department of Radiology, Massachusetts General Brigham, Boston, MA, USA. (12) Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. cgarris@mgh.harvard.edu. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. cgarris@mgh.harvard.edu. (13) David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.

Citation: Nat Biotechnol 2026 May 13 Epub05/13/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42129506

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Differential assembly of mouse and human tumor microenvironments Spotlight 

(1) Courau T (2) Jaszczak RG (3) Samad B (4) Flynn E (5) Chew NW (6) Reeder GC (7) Tsui J (8) Teklu S (9) Pass LF (10) Edwards AW (11) Naser M (12) Ray A (13) Wismer H (14) Bunis D (15) Lupin-Jimenez L (16) Gavil NV (17) Masopust D (18) Graham JP (19) Skelly DA (20) Vesco X (21) Liu ET (22) Fragiadakis GK (23) Combes AJ (24) Krummel MF

Nature immunology

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

Contributed by Shishir Pant

(1) Courau T (2) Jaszczak RG (3) Samad B (4) Flynn E (5) Chew NW (6) Reeder GC (7) Tsui J (8) Teklu S (9) Pass LF (10) Edwards AW (11) Naser M (12) Ray A (13) Wismer H (14) Bunis D (15) Lupin-Jimenez L (16) Gavil NV (17) Masopust D (18) Graham JP (19) Skelly DA (20) Vesco X (21) Liu ET (22) Fragiadakis GK (23) Combes AJ (24) Krummel MF

Nature immunology

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

Contributed by Shishir Pant

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

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

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

Citation: Nat Immunol 2026 May 19 Epub05/19/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42156893

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

(1) Wang N (2) Yeh H (3) Lai B (4) Perera J (5) Jude KM (6) Risch I (7) Um J (8) Chen X (9) Xiang X (10) Wang C (11) Liu LD (12) Yang X (13) Paley MA (14) Khan AA (15) Garcia KC

Nature biotechnology

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

(1) Wang N (2) Yeh H (3) Lai B (4) Perera J (5) Jude KM (6) Risch I (7) Um J (8) Chen X (9) Xiang X (10) Wang C (11) Liu LD (12) Yang X (13) Paley MA (14) Khan AA (15) Garcia KC

Nature biotechnology

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

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

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

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

Citation: Nat Biotechnol 2026 May 13 Epub05/13/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42129507

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