Journal Articles

Molecular heterogeneity and clonal origin of CCR8+ effector regulatory T cells in human cancer

Julian Swatler (1,5), Simone Puccio (1,2,5), Emanuele Voulaz (1,3), Giuseppe Marulli (1,3), Matthew Wheeler (4), Sharmila Sambanthamoorthy (4), Eloise Scamardella (1), Simona Feno (1), Sabina Testa (1), Chiara Camisaschi (1), Emilia Maria Cristina Mazza (1), Enrico Lugli (1)

Nature communications

Swatler, Puccio, et al. used scRNsSeq and flow cytometry to define Treg subsets and analyze heterogeneity and TCR overlap across tissues from 9 tumor types. A common intratumoral Treg signature of 88 genes was defined, and four Treg subsets were further resolved (CCR7+ quiescent; CCR8+ effector; CD161+ Th17-like; intermediate lacking strong functional markers). In NSCLC, CCR8+ effector abundance correlated negatively, and CD161+ Th17-like abundance correlated positively with relapse-free survival. CCR8+ Tregs showed strong tumor localization with clonotype expansion, strong overlap with Tregs in dLN, and some overlap with normal adjacent tissue Tregs and intratumoral Tconv.

Contributed by Ed Fritsch

Julian Swatler (1,5), Simone Puccio (1,2,5), Emanuele Voulaz (1,3), Giuseppe Marulli (1,3), Matthew Wheeler (4), Sharmila Sambanthamoorthy (4), Eloise Scamardella (1), Simona Feno (1), Sabina Testa (1), Chiara Camisaschi (1), Emilia Maria Cristina Mazza (1), Enrico Lugli (1)

Nature communications

Swatler, Puccio, et al. used scRNsSeq and flow cytometry to define Treg subsets and analyze heterogeneity and TCR overlap across tissues from 9 tumor types. A common intratumoral Treg signature of 88 genes was defined, and four Treg subsets were further resolved (CCR7+ quiescent; CCR8+ effector; CD161+ Th17-like; intermediate lacking strong functional markers). In NSCLC, CCR8+ effector abundance correlated negatively, and CD161+ Th17-like abundance correlated positively with relapse-free survival. CCR8+ Tregs showed strong tumor localization with clonotype expansion, strong overlap with Tregs in dLN, and some overlap with normal adjacent tissue Tregs and intratumoral Tconv.

Contributed by Ed Fritsch

ABSTRACT: CD4+CD25+FOXP3+ regulatory T cells (Treg) are highly activated in tumors and promote disease progression. Specific, universal targeting of these effector Treg cells is limited by the lack of a conserved signature across human cancers and information on their origin. Here we combine analysis of single-cell RNA-sequencing datasets with spectral flow cytometry and identify a core signature of 88 genes consistently upregulated in intratumoral Treg cells among 9 epithelial cancers. We describe 4 Treg cell subsets – CCR7+ quiescent, CCR8+ effector, CD161+ and intermediate, with distinct tissue distribution, function, differentiation trajectories and molecular drivers. By single-cell T cell receptor sequencing, we observe that protumoral, effector CCR8+ Treg cells exhibit little clonal relationship with other Treg cell subsets inside tumors, but are clonally related to Treg cells in tumor-draining lymph nodes, as well as conventional T cells in tumors. This resource provides insights for development and fine-tuning of CCR8+ Treg cell-targeting therapies in cancer.

Author Info: 1-IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy. 2- Institute of Genetic and Biomedical Research, UoS Milan, National Research Council, Rozzano, Milan, Italy. 3- Departm

Author Info: 1-IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy. 2- Institute of Genetic and Biomedical Research, UoS Milan, National Research Council, Rozzano, Milan, Italy. 3- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. 4- Discovery Biology, Bristol Myers Squibb Company, Redwood City, California, CA, USA. 5- These authors contributed equally: Julian Swatler, Simone Puccio. e-mail: julian.swatler@humanitasresearch.it; enrico.lugli@humanitasresearch.it

Citation: Nat com 2026

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    Tumor immune microenvironment remodeling predicts response to checkpoint inhibitor therapy

    (1) Lin Z (2) Chandra M (3) Srinivas N (4) Ugurel S (5) Becker JC (6) Aran D

    Cancer cell

    Lin et al. developed a longitudinal scRNAseq atlas of 441 ICI-treated tumors from 241 patients across 10 cancer types, and identified 4 conserved TIME states. ICI induced temporal TIME remodeling with early T cell activation, resolution of distinct ISG patterns, and progressive stromal and immune restructuring. During treatment, approximately 40% of tumors transitioned between TIME states, with inflamed or B cell-enriched transitions associated with response and survival, while myeloid-dominant states associated with resistance. A baseline 59-gene transition signature was predictive of ICI response and survival across 1,383 tumors from 19 independent cohorts.

    Contributed by Shishir Pant

    (1) Lin Z (2) Chandra M (3) Srinivas N (4) Ugurel S (5) Becker JC (6) Aran D

    Cancer cell

    Lin et al. developed a longitudinal scRNAseq atlas of 441 ICI-treated tumors from 241 patients across 10 cancer types, and identified 4 conserved TIME states. ICI induced temporal TIME remodeling with early T cell activation, resolution of distinct ISG patterns, and progressive stromal and immune restructuring. During treatment, approximately 40% of tumors transitioned between TIME states, with inflamed or B cell-enriched transitions associated with response and survival, while myeloid-dominant states associated with resistance. A baseline 59-gene transition signature was predictive of ICI response and survival across 1,383 tumors from 19 independent cohorts.

    Contributed by Shishir Pant

    ABSTRACT: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assemble a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancer entities to map treatment-associated remodeling of the tumor immune microenvironment (TIME). With a hierarchical reference-guided deep-phenotyping framework, we define 77 immune and stromal subtypes and resolve four conserved TIME subtypes. Approximately 40% of tumors shift between states during therapy, and the transition is more predictive of outcome than the baseline state. Favorable transitions toward inflamed or B cell-enriched subtype track with improved response and survival, while persistence in or shifts toward myeloid dominance indicate resistance. We derive a 59-gene signature that predicted response and survival for 1,383 baseline tumors across 19 independent cohorts. These findings establish immunotype transitions as a central determinant of ICI efficacy, offering new avenues for response prediction and rational immunotherapy design.

    Author Info: (1) Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel. (2) Translational Skin Cancer Research, German Cancer Consortium (DKTK), Partner Site Essen, Medical

    Author Info: (1) Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel. (2) Translational Skin Cancer Research, German Cancer Consortium (DKTK), Partner Site Essen, Medical Faculty, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. (3) Translational Skin Cancer Research, German Cancer Consortium (DKTK), Partner Site Essen, Medical Faculty, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Dermatology, University Hospital Essen, Essen, Germany. (4) German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Dermatology, University Hospital Essen, Essen, Germany. (5) Translational Skin Cancer Research, German Cancer Consortium (DKTK), Partner Site Essen, Medical Faculty, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Dermatology, University Hospital Essen, Essen, Germany. Electronic address: j.becker@dkfz-heidelberg.de. (6) Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel; The Taub Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel. Electronic address: dviraran@technion.ac.il.

    Citation: Cancer Cell 2026 Sep 1 Epub09/01/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42679812

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    In vivo genome-wide CRISPR screens of human T cells in solid tumours

    (1) Liu Q (2) Chen PA (3) Urs E (4) Zhang S (5) Arce MM (6) Wang CH (7) Yan J (8) Nguyen VQ (9) Li Z (10) Seo J (11) Kale N (12) Peng F (13) Luo Y (14) Goudy L (15) LaFlam TN (16) Zhong H (17) Modak C (18) Dann E (19) Jung JH (20) Kirane A (21) Warner AB (22) Quach BB (23) Good Z (24) Shy BR (25) Shifrut E (26) Bapat SP (27) Allen GM (28) Eyquem J (29) Fuh K (30) Dodgson SE (31) Cyster JG (32) Marson A (33) Carnevale J

    Nature

    Liu et al. developed a genome-wide in vivo CRISPR screening platform using a T cell-attracting anti-CD3 scFv-expressing A375 tumor model and primary human T cells to identify in vivo regulators of intratumoral T cell abundance and effector function. The abundance screen identified P2RY8-Gα13 as a negative regulator of T cell tumor infiltration, whereas the IFNγ-based functional screen identified GNAS as a key driver of intratumoral T cell dysfunction. GNAS deletion enhanced CAR- and TCR-T cell fitness and efficacy across solid tumor models. P2RY8 and GNAS dual-knockout CAR T cells showed increased infiltration and improved tumor control.

    Contributed by Shishir Pant

    (1) Liu Q (2) Chen PA (3) Urs E (4) Zhang S (5) Arce MM (6) Wang CH (7) Yan J (8) Nguyen VQ (9) Li Z (10) Seo J (11) Kale N (12) Peng F (13) Luo Y (14) Goudy L (15) LaFlam TN (16) Zhong H (17) Modak C (18) Dann E (19) Jung JH (20) Kirane A (21) Warner AB (22) Quach BB (23) Good Z (24) Shy BR (25) Shifrut E (26) Bapat SP (27) Allen GM (28) Eyquem J (29) Fuh K (30) Dodgson SE (31) Cyster JG (32) Marson A (33) Carnevale J

    Nature

    Liu et al. developed a genome-wide in vivo CRISPR screening platform using a T cell-attracting anti-CD3 scFv-expressing A375 tumor model and primary human T cells to identify in vivo regulators of intratumoral T cell abundance and effector function. The abundance screen identified P2RY8-Gα13 as a negative regulator of T cell tumor infiltration, whereas the IFNγ-based functional screen identified GNAS as a key driver of intratumoral T cell dysfunction. GNAS deletion enhanced CAR- and TCR-T cell fitness and efficacy across solid tumor models. P2RY8 and GNAS dual-knockout CAR T cells showed increased infiltration and improved tumor control.

    Contributed by Shishir Pant

    ABSTRACT: Large-scale CRISPR screening in human T cells holds significant promise for identifying genetic modifications that enhance cellular immunotherapy. Yet, many regulators of T cell performance in solid tumours are not revealed in vitro(1,2). In vivo screening in tumour-bearing mice is more physiological but has been limited by low intratumoural T cell recovery. Here we developed an in vivo model that efficiently recovers human T cells from solid tumours, permitting genome-wide CRISPR screens with few mice. Tumour-infiltrating T cells from this model exhibit hallmarks of dysfunction compared with splenic T cells, creating an ideal screening context. We performed two genome-wide CRISPR knockout screens to identify regulators of intratumoural T cell abundance and effector function. The abundance screen revealed the P2RY8-G_13 GPCR signalling axis as a negative regulator of T cell tumour infiltration. The effector function screen identified GNAS as a key driver of T cell dysfunction in tumours, whose product, G_s, acts as a convergent node downstream of multiple GPCRs sensing distinct suppressive ligands. Knockout of GNAS rendered T cells resistant to multiple suppressive cues and significantly improved efficacy across diverse solid tumour models in chimeric antigen receptor (CAR) and T cell receptor (TCR) systems. Combinatorial knockout of P2RY8-GNAS further enhanced tumour control, demonstrating that complementary in vivo screens can identify orthogonal targets whose combined editing improves therapeutic potency. This flexible, scalable platform can be adapted for systematic discovery of genetic strategies to improve solid tumour T cell therapies.

    Author Info: (1) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. qi.liu3@ucsf.edu. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Author Info: (1) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. qi.liu3@ucsf.edu. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. qi.liu3@ucsf.edu. (2) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (3) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (4) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (5) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (6) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (7) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (8) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA. Department of Surgery, University of California San Francisco, San Francisco, CA, USA. Diabetes Center, University of California San Francisco, San Francisco, CA, USA. (9) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (10) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (11) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (12) Department of Microbiology and Immunology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA. (13) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (14) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (15) Department of Microbiology and Immunology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA. Division of Pediatric Rheumatology, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA. (16) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. (17) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (18) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. (19) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (20) Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA. (21) Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. (22) Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA. Division of Computational Medicine, Department of Medicine, Stanford University, Stanford, CA, USA. (23) Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA. Division of Computational Medicine, Department of Medicine, Stanford University, Stanford, CA, USA. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. (24) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. (25) Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel. George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. Dotan Center for Advanced Therapies, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. (26) Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. Department of Pathology, University of California San Francisco, San Francisco, CA, USA. (27) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. (28) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. (29) Department of OB/Gyn, Center for Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA. (30) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (31) Department of Microbiology and Immunology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA. (32) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. Institute for Human Genetics (IHG), University of California San Francisco, San Francisco, CA, USA. Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA. Innovative Genomics Institute, University of California Berkeley, Berkeley, CA, USA. (33) Department of Medicine, University of California San Francisco, San Francisco, CA, USA. julia.carnevale@ucsf.edu. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. julia.carnevale@ucsf.edu. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. julia.carnevale@ucsf.edu. Weill Cancer Hub West, Stanford University and University of California, San Francisco, CA, USA. julia.carnevale@ucsf.edu. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. julia.carnevale@ucsf.edu.

    Citation: Nature 2026 Aug 12 Epub08/12/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42587162

    Tags:

    Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits

    Spotlight 

    (1) Zhu R (2) Dann E (3) Yan J (4) Reyes Retana J (5) Goto R (6) Guitche RC (7) Brixi L (8) Ota M (9) Hartman A (10) Roth TL (11) Satpathy AT (12) Pritchard JK (13) Marson A

    Cell

    Zhu, Dann, et al. developed a transcriptome-wide and transcription factor genome-wide CRISPRi knockdown perturb-seq platform for human CD4+ T cells to comprehensively identify functional gene networks. Four T cell donors were utilized, and deep single-cell RNAseq was conducted under 3 conditions: resting, 8, and 48 hours after stimulation. Multiple patterns (positive and negative; few or many genes affected), context-specific effects (resting vs. stimulated; Th1 vs. Th2), and complex cytokine regulatory patterns were observed. Integration with GWAS studies confirmed and extended known linkages, and revealed new autoimmune targets.

    Contributed by Ed Fritsch

    (1) Zhu R (2) Dann E (3) Yan J (4) Reyes Retana J (5) Goto R (6) Guitche RC (7) Brixi L (8) Ota M (9) Hartman A (10) Roth TL (11) Satpathy AT (12) Pritchard JK (13) Marson A

    Cell

    Zhu, Dann, et al. developed a transcriptome-wide and transcription factor genome-wide CRISPRi knockdown perturb-seq platform for human CD4+ T cells to comprehensively identify functional gene networks. Four T cell donors were utilized, and deep single-cell RNAseq was conducted under 3 conditions: resting, 8, and 48 hours after stimulation. Multiple patterns (positive and negative; few or many genes affected), context-specific effects (resting vs. stimulated; Th1 vs. Th2), and complex cytokine regulatory patterns were observed. Integration with GWAS studies confirmed and extended known linkages, and revealed new autoimmune targets.

    Contributed by Ed Fritsch

    ABSTRACT: Gene regulatory networks encode the fundamental logic of cellular functions, but systematic network mapping remains challenging, especially in cell states relevant to human biology and disease. Here, we perturbed all expressed genes across 22 million primary human CD4(+) T cells from four donors and developed a probe-based perturb-seq platform to measure the transcriptome effects in cells at rest and after stimulation. These data allowed us to map genes regulating immune pathways, including previously uncharacterized regulators of cytokine production. Importantly, active regulators and the gene programs they control changed dramatically across stimulation conditions. Perturbation signatures enabled us to model T cell states observed in population-scale transcriptomic atlases, nominating regulators of T cell polarization and of age-related phenotypes. Finally, we leveraged perturb-seq to implicate context-specific gene regulatory pathways in autoimmune disease risk. Our study provides a foundational resource and new approaches to decode T cell function and human immune traits.

    Author Info: (1) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA. Electronic address: ronghui.zhu@gladston

    Author Info: (1) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA. Electronic address: ronghui.zhu@gladstone.ucsf.edu. (2) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA. Electronic address: emmadann@stanford.edu. (3) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (4) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA. (5) Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. (6) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; University of San Francisco, San Francisco, CA, USA. (7) Department of Genetics, Stanford University, Stanford, CA, USA. (8) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA; Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. (9) Department of Genetics, Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA. (10) Department of Pathology, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Weill Foundation West Coast Cancer Hub, Stanford, CA, USA. (11) Department of Genetics, Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Weill Foundation West Coast Cancer Hub, Stanford, CA, USA. (12) Department of Genetics, Stanford University, Stanford, CA, USA; Department of Biology, Stanford University, Stanford, CA, USA. Electronic address: pritch@stanford.edu. (13) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Weill Foundation West Coast Cancer Hub, Stanford, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. Electronic address: alex.marson@gladstone.ucsf.edu.

    Citation: Cell 2026 Aug 28 Epub08/28/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42664972

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    Engineered human iPSC-derived dendritic cells dressed with tumor MHC complexes as a cancer vaccine Spotlight 

    (1) Xu H (2) Kamei C (3) Gškbuget D (4) Du J (5) Barrero M (6) Wang L (7) Iwamoto Y (8) Soriano JM (9) Godding K (10) Wang B (11) Kwek SS (12) Nistor GI (13) Keirstead HS (14) Fong L (15) Kaneko S (16) Gaensler KML (17) Blelloch R

    Cell stem cell

    Xu et al. identified optimal and reproducible culture and cytokine conditions to create universal, human iPSC-derived DCs lacking HLA (B2m/CTIIA KO) and with a CCR7+ migratory DC phenotype. An exosome-inspired process with tumor cell vesicles was used to cross-dress these DCs with peptide:MHC complexes from tumor cells. Cross-dressing promoted antigen presentation and resistance to “non-self” killing by NK cells. With cell lines in vitro and in vivo, the cross-dressed DCs enhanced T cell cytotoxicity and tumor control. Personal cross-dressed vaccines in AML and ovarian cancer were cytolytic in vitro. PD-L1/2 knockout improved activity.

    Contributed by Ed Fritsch

    (1) Xu H (2) Kamei C (3) Gškbuget D (4) Du J (5) Barrero M (6) Wang L (7) Iwamoto Y (8) Soriano JM (9) Godding K (10) Wang B (11) Kwek SS (12) Nistor GI (13) Keirstead HS (14) Fong L (15) Kaneko S (16) Gaensler KML (17) Blelloch R

    Cell stem cell

    Xu et al. identified optimal and reproducible culture and cytokine conditions to create universal, human iPSC-derived DCs lacking HLA (B2m/CTIIA KO) and with a CCR7+ migratory DC phenotype. An exosome-inspired process with tumor cell vesicles was used to cross-dress these DCs with peptide:MHC complexes from tumor cells. Cross-dressing promoted antigen presentation and resistance to “non-self” killing by NK cells. With cell lines in vitro and in vivo, the cross-dressed DCs enhanced T cell cytotoxicity and tumor control. Personal cross-dressed vaccines in AML and ovarian cancer were cytolytic in vitro. PD-L1/2 knockout improved activity.

    Contributed by Ed Fritsch

    ABSTRACT: Autologous-derived dendritic cells (DCs) are a promising source for cell-based cancer vaccines. However, their therapeutic potential is challenged by the number and quality produced and the diversity of antigens presented. To address these limitations, we present an approach that involves differentiating universal MHC-deficient human-induced pluripotent stem cells (hiPSCs) into CCR7(+) migratory DCs. These DCs are subsequently "dressed" with the full repertoire of MHC-antigen complexes derived from tumor cell membranes, transforming an allogeneic substrate into a personalized cancer vaccine product. The resulting "TumorDressed" DCs effectively activate T cells against tumor antigens. Their function is diminished when CD80/86 is deleted but significantly enhanced by the loss of PD-L1/2. PD-L1/2-null TumorDressed DCs demonstrate robust priming of anti-tumor T cell-mediated cytotoxicity both in vitro and in vivo, including against primary hematologic and solid tumors with matching patient T cells. These findings provide proof of concept for a universal, scalable, adaptable, and off-the-shelf DC cancer vaccine platform.

    Author Info: (1) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, Univ

    Author Info: (1) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (2) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (3) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (4) Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA. (5) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (6) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (7) Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan. (8) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. (9) AIVITA Biomedical, Irvine, CA, USA. (10) Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan. (11) Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA; Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA. (12) AIVITA Biomedical, Irvine, CA, USA. (13) AIVITA Biomedical, Irvine, CA, USA. (14) Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA; Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA. (15) Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan. (16) Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA. (17) Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, San Francisco (UCSF), San Francisco, CA, USA; Department of Urology, University of California, San Francisco (UCSF), San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (UCSF), San Francisco, CA, USA. Electronic address: robert.blelloch@ucsf.edu.

    Citation: Cell Stem Cell 2026 Aug 25 Epub08/25/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42641609

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    Scalable generation of hematopoietic stem cell-engineered off-the-shelf mono-specific cytotoxic T cells targeting solid tumors Spotlight 

    (1) Zhu Y (2) Yu J (3) Kim YJ (4) Tian Y (5) Li Z (6) Chen Y (7) Lyu Z (8) Zhu E (9) Zhao AS (10) Ma N (11) Zhang C (12) Kramer A (13) Wilson M (14) Hon R (15) Wang YC (16) Lin S (17) Shen X (18) Hahn Z (19) Zhang Y (20) Wang A (21) Li YR (22) Yang L

    Cell reports. Medicine

    Zhu and Yu et al. developed and characterized a scalable, feeder-free, high-yield platform that generated allogeneic NY-ESO-1-specific cytotoxic T (AlloESO-T) cells from cord blood-derived hematopoietic stem and progenitor cells. Compared to PBMC-derived TCR-T cells, monospecific AlloESO-T cells showed superior cytotoxicity (with dual TCR and NKR targeting), solid tumor homing, HLA-independence, durable killing persistence (with IL-15), and enhanced efficacy in solid tumor models. AlloESO-T cells, which lack endogenous TCRs, exhibited an improved safety profile and minimal GvHD or /cytokine release syndrome risk in vitro and in vivo.

    Contributed by Katherine Turner

    (1) Zhu Y (2) Yu J (3) Kim YJ (4) Tian Y (5) Li Z (6) Chen Y (7) Lyu Z (8) Zhu E (9) Zhao AS (10) Ma N (11) Zhang C (12) Kramer A (13) Wilson M (14) Hon R (15) Wang YC (16) Lin S (17) Shen X (18) Hahn Z (19) Zhang Y (20) Wang A (21) Li YR (22) Yang L

    Cell reports. Medicine

    Zhu and Yu et al. developed and characterized a scalable, feeder-free, high-yield platform that generated allogeneic NY-ESO-1-specific cytotoxic T (AlloESO-T) cells from cord blood-derived hematopoietic stem and progenitor cells. Compared to PBMC-derived TCR-T cells, monospecific AlloESO-T cells showed superior cytotoxicity (with dual TCR and NKR targeting), solid tumor homing, HLA-independence, durable killing persistence (with IL-15), and enhanced efficacy in solid tumor models. AlloESO-T cells, which lack endogenous TCRs, exhibited an improved safety profile and minimal GvHD or /cytokine release syndrome risk in vitro and in vivo.

    Contributed by Katherine Turner

    ABSTRACT: Adoptive T cell therapy for solid tumors is limited by autologous manufacturing complexity and, in allogeneic settings, risks including graft-versus-host disease (GvHD), HLA restriction, and donor variability. We develop a scalable, feeder-free platform to differentiate gene-engineered hematopoietic stem and progenitor cells (HSPCs) into allogeneic, NY-ESO-1-specific cytotoxic T ((Allo)ESO-T) cells. Product phenotype, function, tumor homing, and safety are assessed against solid tumor models and benchmarked to peripheral blood mononuclear cell (PBMC)-derived TCR-engineered T cells. (Allo)ESO-T cells display a uniform cytotoxic phenotype, with dual tumor targeting through a transgenic TCR and natural killer receptors. Relative to PBMC-derived counterparts, (Allo)ESO-T cells show superior cytotoxicity, selective solid-tumor homing, durable killing persistence, and resilience to immune evasion. They also maintain low GvHD and cytokine release syndrome risk, while retaining stable hypoimmunogenic features. These findings establish HSPC-derived (Allo)ESO-T cells as an off-the-shelf, mono-specific cytotoxic T cell therapy with scalable manufacturing, enhanced efficacy, and improved safety, which support broad applicability of (Allo)ESO-T cells across solid tumors.

    Author Info: (1) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los A

    Author Info: (1) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (2) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (3) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (4) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (5) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (6) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (7) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (8) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (9) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (10) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (11) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (12) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (13) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (14) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (15) Department of Medicine, Division of Cardiology, UCLA, Los Angeles, CA 90095, USA. (16) Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA. (17) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (18) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (19) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. (20) Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA. (21) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA. Electronic address: charlie.li@ucla.edu. (22) Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Bioengineering, UCLA, Los Angeles, CA 90095, USA; Eli and Edythe Broad Centre of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA; Molecular Biology Institute, UCLA, Los Angeles, CA 90095, USA; Parker Institute for Cancer Immunotherapy, UCLA, Los Angeles, CA 90095, USA; Goodman-Luskin Microbiome Center, UCLA, Los Angeles, CA 90095, USA. Electronic address: liliyang@ucla.edu.

    Citation: Cell Rep Med 2026 Aug 19 102998 Epub08/19/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42617602

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    Cancer Immunotherapy Using AIRE Conditioning of the Tumor Epitopeome Featured  

    (1) Chen A (2) Pulido JS (3) Tonne J (4) Metko M (5) Thompson JM (6) Sangsuwannukul T (7) Chiriboga-Yerovi MP (8) Diaz RM (9) Webb MJ (10) Huff AL (11) Moore M (12) Schuelke MR (13) Irshad S (14) Appleton E (15) Melcher A (16) Kendall B (17) Vile RG

    Cancer immunology research

    Chen, Pulido, et al. investigated how AIRE expression in tumor cells impacts antitumor immune responses using murine models. Overexpression of AIRE led to higher expression of self-proteins, MHC-I in the context of H-2Kb, and MHC-I-presented epitopes, whereas downregulation led to lower expression. Antitumor immunity could be induced by DC vaccines loaded with cell lysates of AIRE-overexpressing tumor cells, inducing CD8+ and CD4+ T cell responses. Therapeutic AIRE tumor expression could be induced with in vivo delivery of an AAV vector, which was effective in curing mice, and survival time was improved by subsequent ICB.

    (1) Chen A (2) Pulido JS (3) Tonne J (4) Metko M (5) Thompson JM (6) Sangsuwannukul T (7) Chiriboga-Yerovi MP (8) Diaz RM (9) Webb MJ (10) Huff AL (11) Moore M (12) Schuelke MR (13) Irshad S (14) Appleton E (15) Melcher A (16) Kendall B (17) Vile RG

    Cancer immunology research

    Chen, Pulido, et al. investigated how AIRE expression in tumor cells impacts antitumor immune responses using murine models. Overexpression of AIRE led to higher expression of self-proteins, MHC-I in the context of H-2Kb, and MHC-I-presented epitopes, whereas downregulation led to lower expression. Antitumor immunity could be induced by DC vaccines loaded with cell lysates of AIRE-overexpressing tumor cells, inducing CD8+ and CD4+ T cell responses. Therapeutic AIRE tumor expression could be induced with in vivo delivery of an AAV vector, which was effective in curing mice, and survival time was improved by subsequent ICB.

    ABSTRACT: T-cell immune tolerance is established in part through the activity of the Auto-immune Regulator (AIRE) transcription factor in the medullary thymic epithelial cells (mTEC) of the thymus. AIRE induces expression of peripheral tissue-specific self-antigens for presentation to nave T cells to promote activation/deletion of autoreactive T cells. This traditional role of AIRE in mTECs is to prevent autoimmunity. Herein, we demonstrate that tumors mimic the role of AIRE in mTECs to evade immune rejection. We found that AIRE induced a profile of "selfness" at the RNA and protein levels which, when presented on major histocompatibility complexes, shielded the tumor from inherently self-tolerized T cells. Moreover, we describe an in vivo immunotherapy in which engineered changes in AIRE expression in tumor cells altered their profile of selfness, exposing both AIRE-modified and parental unmodified tumor cells to T-cell attack. Therefore, by re-setting the immunological selfness of cancer cells, this AIRE-mediated immunotherapy 1) converted a highly tolerized T-cell compartment into a tumor-reactive T-cell population; 2) conferred upon non-immunogenic tumors de novo sensitivity to immune checkpoint blockade; 3) removed the need to identify potentially immunogenic tumor-associated antigens as targets for generation of T-cell responses; and 4) lead to potent T cell-mediated rejection of aggressive, immunologically cold, non-immunogenic tumors. Patient RNA-sequencing data showed that expression of AIRE predicted response to immune therapies with a strong correlation between AIRE expression and markers of T-cell receptor signaling, suggesting our studies have therapeutic translational value.

    Author Info: (1) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (2) Wills Eye Hospital Philadelphia, PA United States. ROR: https://ror.org/03qygnx22 (3) Mayo Clinic Ro

    Author Info: (1) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (2) Wills Eye Hospital Philadelphia, PA United States. ROR: https://ror.org/03qygnx22 (3) Mayo Clinic Rochester, Minnesota United States. ROR: https://ror.org/02qp3tb03 (4) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (5) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (6) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (7) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (8) Vyriad United States. (9) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (10) Johns Hopkins Medicine Baltimore United States. ROR: https://ror.org/037zgn354 (11) Mayo Clinic Rochester, Minnesota United States. ROR: https://ror.org/02qp3tb03 (12) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03 (13) King's College London London United Kingdom. ROR: https://ror.org/0220mzb33 (14) Institute of Cancer Research London United Kingdom. ROR: https://ror.org/043jzw605 (15) Institute of Cancer Research London United Kingdom. ROR: https://ror.org/043jzw605 (16) Mayo Clinic Rochester, Minnesota United States. ROR: https://ror.org/02qp3tb03 (17) Mayo Clinic Rochester, MN United States. ROR: https://ror.org/02qp3tb03

    Citation: Cancer Immunol Res 2026 Aug 26 Epub08/26/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42644627

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    Synthetic transcription factors designed by domain recombination enhance CAR T cell antitumor function Spotlight 

    (1) Takacsi-Nagy O (2) Kasinathan S (3) Hartman A (4) Yin Y (5) Wu L (6) Chen AY (7) Moser LM (8) May AP (9) Reeder GC (10) Celallos Fuentes E (11) Kernick C (12) Lu J (13) McClellan AK (14) Raposo CJ (15) Terrall B (16) Theberath NE (17) Yan PK (18) Xu P (19) Sotillo E (20) Eyquem J (21) Mackall CL (22) Roth TL (23) Satpathy AT

    Cell

    Takacsi-Nagy et al. generated a library of synthetic T cell Transcription Factors (sTFs) through combinatorial assembly of AP-1 family TF subdomains. Expressed in CAR-T cells, certain sTFs improved CAR-T persistence/proliferation over natural TFs in a chronic stimulation assay. sTFs induced unique transcriptional and epigenetic T cell states from natural TFs, although their DNA binding sites were conserved. One combination (JUN-FOS-BATF) especially improved cytotoxicity and in vivo persistence. The most impactful subdomains did not correlate with natural expression levels. Recombination of ETS and FOX family TF domains was also effective.

    Contributed by Alex Najibi

    (1) Takacsi-Nagy O (2) Kasinathan S (3) Hartman A (4) Yin Y (5) Wu L (6) Chen AY (7) Moser LM (8) May AP (9) Reeder GC (10) Celallos Fuentes E (11) Kernick C (12) Lu J (13) McClellan AK (14) Raposo CJ (15) Terrall B (16) Theberath NE (17) Yan PK (18) Xu P (19) Sotillo E (20) Eyquem J (21) Mackall CL (22) Roth TL (23) Satpathy AT

    Cell

    Takacsi-Nagy et al. generated a library of synthetic T cell Transcription Factors (sTFs) through combinatorial assembly of AP-1 family TF subdomains. Expressed in CAR-T cells, certain sTFs improved CAR-T persistence/proliferation over natural TFs in a chronic stimulation assay. sTFs induced unique transcriptional and epigenetic T cell states from natural TFs, although their DNA binding sites were conserved. One combination (JUN-FOS-BATF) especially improved cytotoxicity and in vivo persistence. The most impactful subdomains did not correlate with natural expression levels. Recombination of ETS and FOX family TF domains was also effective.

    Contributed by Alex Najibi

    ABSTRACT: Human protein-coding genes evolved via rearrangement of domains from ancestral genes. We develop a scalable, evolutionarily guided method to assemble novel genes from constituent domains within a protein family, termed DESynR (domain engineered via synthesis and recombination) genes. In primary human T cells, DESynR activator protein-1 (AP-1) transcription factors (TFs) significantly outperform natural AP-1 TFs across in vitro and in vivo antitumor assays. DESynR AP-1 TFs induce broad transcriptional and epigenetic reprogramming and establish non-natural T cell states that optimize features of exhaustion, effector and cytotoxic function, and persistence-sometimes co-opting gene modules from disparate cell types. Reprogramming is primarily driven by differential regulation of established AP-1-bound regulatory elements rather than unique binding. Finally, we screen DESynR erythroblast transformation-specific (ETS) and forkhead box (FOX) TFs to support generalizability across protein families. Overall, we demonstrate that reconfiguring existing protein domains may uncover non-evolved genes that program therapeutically relevant cell states.

    Author Info: (1) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford Univer

    Author Info: (1) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA. (2) Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA. (3) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA. (4) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (5) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (6) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA. (7) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (8) Program in Immunology, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. (9) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA. (10) Department of Pathology, Stanford University, Stanford, CA, USA. (11) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (12) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (13) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (14) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA. (15) Department of Pathology, Stanford University, Stanford, CA, USA. (16) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA. (17) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA. (18) Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. (19) Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA; Weill Cancer Hub West, Stanford, CA, USA. (20) Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Weill Cancer Hub West, Stanford, CA, USA. (21) Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA; Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, CA, USA; Weill Cancer Hub West, Stanford, CA, USA. (22) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Weill Cancer Hub West, Stanford, CA, USA. Electronic address: troth@stanford.edu. (23) Department of Pathology, Stanford University, Stanford, CA, USA; Center for Immunotherapy Design, Stanford University, Stanford, CA, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Weill Cancer Hub West, Stanford, CA, USA. Electronic address: satpathy@stanford.edu.

    Citation: Cell 2026 Aug 19 Epub08/19/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42617595

    Tags:

    Spatiotemporal multiomics uncover tumor ecosystem dynamics during metastatic colonization Spotlight 

    (1) Sun Y (2) Zhong Y (3) Liu S (4) Zhang Z (5) Wang C (6) Liu Y (7) Chen J (8) Guo W (9) Gu X (10) Rao K (11) Wang Z (12) Cao M (13) Wang Y (14) Huang W (15) Zou X (16) Chen X (17) Qiu S (18) Shi Y (19) Sun H (20) Huang X (21) Wang Y (22) Wang J (23) Wu Z (24) Tian R (25) Zhang Y (26) Gu J (27) Jiang M (28) Bai Y (29) Li G (30) Xie M (31) Xi F (32) Peng L (33) Liu S (34) Yang S (35) Zhang Y (36) Esteban MA (37) Jin X (38) Chen A (39) Wang J (40) Cang Y (41) Peng DH (42) Xu X (43) Zhou J (44) Wu L (45) Fan J

    Science

    Sun et al. performed multi-omics analysis across nine stages of mouse HCC lung metastatic colonization, with supporting human data, to map the co-evolution of disseminated tumor cells (DTCs) and host immune niches. A rare transient, quiescent subpopulation of Phgdhhigh DTCs survived neutrophil- and NK cell-mediated clearance. PHGDH-driven one-carbon metabolism increased S-adenosylmethionine and H3K27me3-mediated silencing of Ccl2 and Cxcl10 to establish an immune-scarce niche. Eventually, CX3CR1high interstitial macrophages accumulated, recruited immunosuppressive cells, and activated IGF1–IGF1R signaling to promote DTC outgrowth.

    Contributed by Shishir Pant

    (1) Sun Y (2) Zhong Y (3) Liu S (4) Zhang Z (5) Wang C (6) Liu Y (7) Chen J (8) Guo W (9) Gu X (10) Rao K (11) Wang Z (12) Cao M (13) Wang Y (14) Huang W (15) Zou X (16) Chen X (17) Qiu S (18) Shi Y (19) Sun H (20) Huang X (21) Wang Y (22) Wang J (23) Wu Z (24) Tian R (25) Zhang Y (26) Gu J (27) Jiang M (28) Bai Y (29) Li G (30) Xie M (31) Xi F (32) Peng L (33) Liu S (34) Yang S (35) Zhang Y (36) Esteban MA (37) Jin X (38) Chen A (39) Wang J (40) Cang Y (41) Peng DH (42) Xu X (43) Zhou J (44) Wu L (45) Fan J

    Science

    Sun et al. performed multi-omics analysis across nine stages of mouse HCC lung metastatic colonization, with supporting human data, to map the co-evolution of disseminated tumor cells (DTCs) and host immune niches. A rare transient, quiescent subpopulation of Phgdhhigh DTCs survived neutrophil- and NK cell-mediated clearance. PHGDH-driven one-carbon metabolism increased S-adenosylmethionine and H3K27me3-mediated silencing of Ccl2 and Cxcl10 to establish an immune-scarce niche. Eventually, CX3CR1high interstitial macrophages accumulated, recruited immunosuppressive cells, and activated IGF1–IGF1R signaling to promote DTC outgrowth.

    Contributed by Shishir Pant

    ABSTRACT: The mechanisms underlying the interactions between disseminated tumor cells (DTCs) and their tissue microenvironment during metastatic colonization are currently poorly understood. We integrated multimodal single-cell and spatial profiling from liver cancer mouse models and human metastases to track the spatiotemporal dynamics of DTCs and their microenvironments from single-cell seeding to overt lung metastasis. We identified a residual population of quiescent Phgdh(high) DTCs that survived initial innate immune clearance and became transiently enriched in micrometastases. These cells shaped an immune-scarce microenvironment through PHGDH-dependent, H3K27me3-mediated epigenetic silencing of chemokine transcription, thereby promoting metastatic expansion. Cx3cr1(high) interstitial macrophages were also transiently enriched before DTC expansion, creating an immune-privileged niche for metastatic outgrowth by recruiting immunosuppressive cells. Inactivating the PHGDH-H3K27me3 axis in DTCs or depleting interstitial macrophages restored immune surveillance and inhibited metastatic colonization. These findings provide insights into the development of micrometastasis-targeting regimens.

    Author Info: (1) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Insti

    Author Info: (1) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (2) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. (3) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Ruijin Yangtze River Delta Health Institute, Wuxi Branch of Ruijin Hospital, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (4) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (5) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (6) School of Life Science and Technology, ShanghaiTech University, Shanghai, China. (7) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (8) Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. (9) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (10) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (11) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (12) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (13) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (14) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (15) BGI Research, Chongqing, China. Department of Neurology, Hubei Provincial Clinical Research Center for Parkinson's Disease, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China. (16) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (17) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (18) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (19) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (20) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (21) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (22) Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. (23) Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. First Hospital of Shanxi Medical University, Taiyuan, China. Molecular Imaging Precision Medical Collaborative Innovation Center, Shanxi Medical University, Taiyuan, China. (24) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (25) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (26) Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. (27) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (28) BGI Research, Hangzhou, China. (29) BGI Research, Chongqing, China. (30) BGI Research, Chongqing, China. (31) BGI Research, Chongqing, China. (32) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (33) BGI Research, Hangzhou, China. (34) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. BGI, Shenzhen, China. (35) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. Department of Oral and Maxillofacial Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Stomatology, Zhongshan Hospital Fudan University, Shanghai, China. (36) 3DC STAR Lab, BGI CELL, Shenzhen, China. Prince Fahad bin Sultan Research Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia. (37) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (38) BGI Research, Chongqing, China. JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing, China. (39) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (40) School of Life Science and Technology, ShanghaiTech University, Shanghai, China. (41) Dunwill Med-Tech, Shanghai, China. (42) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (43) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (44) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (45) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China.

    Citation: Science 2026 Jul 30 393:eadz7928 Epub07/30/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42531396

    Tags:

    Subclinical cholestasis is a hallmark of gut dysbiosis causing resistance to cancer immunotherapy

    Spotlight 

    Anne-Laure Mallard de La Varende (1); Ai-Ling Tian,1 Simon Thomas (1); Imran Lahmar (1); Meriem Messaoudene (2); Sijing Li (3); Omar Motiño (3,4); Hortense Guillaume-dit-Taunière (1); Valerio Iebba (1); Yoan Hurtado (1); Thao-Nguyen Pham (1); Cassandra Thélémaque (1); Miguel Araujo-Voces (1); Deborah Suissa (1); Pierre Ly (1); Ella Reich (1); Giacomo Vitali (5); Bryan Thierry Arlunno (1); Sylvere Durand (6); Fanny Aprahamian (6); Marion Leduc (3,6); Sabrina Forveille (3,6); Oliver Kepp (3,6); Carlos de la Calle-Fabregat (7); Angela Schippers (8); Norbert Wagner (8); Pierre-Edouard Fournier (9); Kenya Honda (10,11,12); Federica Marmorino (13); Chiara Cremolini (13,14); Saman Maleki Vareki (15,16,17,18).

    Cancer Cell

    Mallard de La Varende et al. showed that gut dysbiosis following treatment with antibiotics or antibiotic-associated species led to loss of secondary bile acids (BAs), increased tauro-conjugated primary BAs, downregulation of MAdCAM-1 in the ilium, and increased γ-glutamyl transferase (γ-GT) in serum, supporting TIME reprogramming, T cell exhaustion, and resistance to anti-PD-1 in tumor-bearing mice. Resistance could be overcome by performing FMT, supplementing secondary BAs, or using an ilium-specific FXR agonist. In patients, resistance was associated with subclinical cholestasis (elevated γ-GT), which predicted poor response.

    Contributed by Lauren Hitchings

    Anne-Laure Mallard de La Varende (1); Ai-Ling Tian,1 Simon Thomas (1); Imran Lahmar (1); Meriem Messaoudene (2); Sijing Li (3); Omar Motiño (3,4); Hortense Guillaume-dit-Taunière (1); Valerio Iebba (1); Yoan Hurtado (1); Thao-Nguyen Pham (1); Cassandra Thélémaque (1); Miguel Araujo-Voces (1); Deborah Suissa (1); Pierre Ly (1); Ella Reich (1); Giacomo Vitali (5); Bryan Thierry Arlunno (1); Sylvere Durand (6); Fanny Aprahamian (6); Marion Leduc (3,6); Sabrina Forveille (3,6); Oliver Kepp (3,6); Carlos de la Calle-Fabregat (7); Angela Schippers (8); Norbert Wagner (8); Pierre-Edouard Fournier (9); Kenya Honda (10,11,12); Federica Marmorino (13); Chiara Cremolini (13,14); Saman Maleki Vareki (15,16,17,18).

    Cancer Cell

    Mallard de La Varende et al. showed that gut dysbiosis following treatment with antibiotics or antibiotic-associated species led to loss of secondary bile acids (BAs), increased tauro-conjugated primary BAs, downregulation of MAdCAM-1 in the ilium, and increased γ-glutamyl transferase (γ-GT) in serum, supporting TIME reprogramming, T cell exhaustion, and resistance to anti-PD-1 in tumor-bearing mice. Resistance could be overcome by performing FMT, supplementing secondary BAs, or using an ilium-specific FXR agonist. In patients, resistance was associated with subclinical cholestasis (elevated γ-GT), which predicted poor response.

    Contributed by Lauren Hitchings

    ABSTRACT: Gut dysbiosis compromises cancer immunosurveillance by downregulating ileal mucosal addressin cell adhesion molecule 1 (MAdCAM-1), but the metabolic landscape associated with gut dysbiosis remains elusive. Here, we show that antibiotics (ABX) or ABX-associated Enterocloster species lead to the loss of secondary bile acids (BAs) including deoxycholic acid (DCA) and the accumulation of tauro-conjugated primary BAs (tauro-chenodeoxycholic acid [TCDCA] and tauro-β-muricholic acid [T-βMCA]) from the alternative pathway in the plasma of patients and mice. Fecal microbial transplantation (FMT), the ileum-specific farnesoid X receptor (FXR) agonist fexaramine, or glycodeoxycholic acid (GDCA) compen- sated dysbiosis-associated BA abnormalities and circumvent primary resistance to PD-1 blockade. GDCA curtailed ABX-induced MAdCAM-1 downregulation and T cell exhaustion in tumors. Subclinical cholestasis defined by elevation of γ-glutamyl transferase (γGT) correlated with increased TCDCA and decreased sMAdCAM-1 in plasma and predicted poor survival in multivariate analyses in six cohorts of patients who received immunotherapy. Hence, subclinical cholestasis accompanies gut dysbiosis, paving the way to immunoresistance.

    Author Info: 1- Université Paris-Saclay, Gustave Roussy (GRCC), ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, 94805 Villejuif, France. 2- Centre de rec

    Author Info: 1- Université Paris-Saclay, Gustave Roussy (GRCC), ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, 94805 Villejuif, France. 2- Centre de recherche Du CHUM (CRCHUM), Montréal, QC H2W1T8, Canada. 3- Centre de Recherche des Cordeliers, INSERM U1138, Equipe Labellisée – Ligue Nationale Contre le Cancer, Université Paris Cité, Sorbonne Université, 75006 Paris, France. 4- Unidad de Excelencia, Instituto de Biomedicina y Genética Molecular de Valladolid, Consejo Superior de Investigaciones Científicas-Universidad de Valladolid, 47001 Valladolid, Spain. 5- MetaGenoPolis, INRAe, Université Paris-Saclay 78350 Jouy en Josas, France. 6- Université Paris-Saclay, INSERM US23, Analyse moléculaire, modélisation et imagerie de la maladie Cancéreuse, Plateformes de Métabolomique et de Criblage Cellulaire Haut Débit, 94805 Villejuif, France. 7- Université Paris-Saclay, Gustave Roussy, U1356 Next Generation Immuno-Oncology Research, 94805 Villejuif, France

    Citation: Cancer Cell July 2026

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