Journal Articles

ANKRD11 deficiency reprograms CD8+ T cell differentiation to enhance immunity in chronic infection and cancer

Using novel Tg mouse models, Xu et al. identified a TCR specific for a clinically relevant epitope rarely expressed on CD8+ T cells in chronic HBV infection. Efficacy of the specific TCR+ CD8+ T cells in a murine chronic HBV infection model was restrained by the immunosuppressive liver environment. Genome-wide CRISPR-Cas9 KO screening identified the mediator of such T cell restraint as ankyrin repeat domain11 (Ankrd11), a chromatin regulator that acts to break AP-1 family gene transcription. Ankrd11 deficiency in T cells boosted T cell expansion and differentiation of TPEX and PD-1-TOX- tolerant cells into effectors of murine antiviral and antitumor responses.

Contributed by Paula Hochman

Using novel Tg mouse models, Xu et al. identified a TCR specific for a clinically relevant epitope rarely expressed on CD8+ T cells in chronic HBV infection. Efficacy of the specific TCR+ CD8+ T cells in a murine chronic HBV infection model was restrained by the immunosuppressive liver environment. Genome-wide CRISPR-Cas9 KO screening identified the mediator of such T cell restraint as ankyrin repeat domain11 (Ankrd11), a chromatin regulator that acts to break AP-1 family gene transcription. Ankrd11 deficiency in T cells boosted T cell expansion and differentiation of TPEX and PD-1-TOX- tolerant cells into effectors of murine antiviral and antitumor responses.

Contributed by Paula Hochman

ABSTRACT: CD8(+) T cell dysfunction is a major obstacle to hepatitis B virus (HBV) clearance and antitumor immunity. Here, using a humanized mouse model, we identify a T cell receptor targeting a clinically relevant HBV epitope and reveal ANKRD11 as a key epigenetic regulator of CD8(+) T cell dysfunction in chronic infection and tumors. Ankrd11 knockout in CD8(+) T cells enhances HBV-specific T cell proliferation and effector differentiation, especially under immunosuppressive conditions, via AP-1 family gene upregulation. Loss of Ankrd11 both drives the conversion of progenitor exhausted T cells into terminally exhausted T cells, and reprograms PD-1(-)TOX(-) tolerant cells into functional effectors, improving antiviral and antitumor responses. Ankrd11-deficient T cells show increased granzyme and superior effector function, enhancing viral control and tumor regression. These findings position ANKRD11 as a promising immunotherapy target for chronic HBV infection and cancer.

Author Info: (1) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Acad

Author Info: (1) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (2) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. (3) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (4) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (5) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (6) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (7) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (8) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. Medical School, University of Chinese Academy of Sciences, Beijing, China. (9) Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing, China. (10) Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China. wuhm2000@sina.com. HBV Infection, Clinical Cure and Immunology Joint Laboratory for Clinical Medicine, Capital Medical University, Beijing, China. wuhm2000@sina.com. Department of Hepatology Division 2, Peking University Ditan Teaching Hospital, Beijing, China. wuhm2000@sina.com. (11) Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China. zhouxy@im.ac.cn. Medical School, University of Chinese Academy of Sciences, Beijing, China. zhouxy@im.ac.cn.

Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

St. Paul and Hendrikse et al. used single-cell sequencing and developed a machine-learning algorithm (PreGame) to distinguish the enigmatic tumor-reactive γδ T cells (TRγδ T cells) from bystander cells and confirm their specificity. From MM patient bone marrow, PreGame identified TRγδ T cells that recognized broadly expressed tumor antigens in a TCR-dependent manner. Responsive patients treated with a BCMA-targeted ADC exhibited significant expansion of TRγδ T cells and of γδ TCRs in cfDNA, which correlated with favorable responses and served as an early response biomarker. A γδ TCR epitope in the ubiquitously expressed HLA-C protein was identified.

Contributed by Katherine Turner

St. Paul and Hendrikse et al. used single-cell sequencing and developed a machine-learning algorithm (PreGame) to distinguish the enigmatic tumor-reactive γδ T cells (TRγδ T cells) from bystander cells and confirm their specificity. From MM patient bone marrow, PreGame identified TRγδ T cells that recognized broadly expressed tumor antigens in a TCR-dependent manner. Responsive patients treated with a BCMA-targeted ADC exhibited significant expansion of TRγδ T cells and of γδ TCRs in cfDNA, which correlated with favorable responses and served as an early response biomarker. A γδ TCR epitope in the ubiquitously expressed HLA-C protein was identified.

Contributed by Katherine Turner

ABSTRACT: γδ T cells are becoming increasingly appreciated for their antitumour capacity and role in mediating responses to immune checkpoint blockade1-3. Unlike classical αβ T cells, the degree to which γδ T cells rely on their T cell receptors (TCRs) to induce antitumour responses remains unclear. The challenge of distinguishing γδ T cells with tumour-reactive TCRs from bystander γδ T cells limits our understanding of tumour-reactive γδ T cell biology and the translation of their TCRs into immunotherapeutics. Here we present PreGame, a machine-learning algorithm capable of identifying tumour-reactive γδ T cells from single-cell CITE sequencing data. We use PreGame to identify tumour-reactive γδ T cells from patients with multiple myeloma or other solid cancers, and confirm the specificity of their TCRs to tumour cells. Clinically, we demonstrate that expansion of tumour-reactive γδ T cells is an early biomarker of response in patients with multiple myeloma receiving combination therapy with belantamab mafodotin. We also identify a γδ TCR epitope in the ubiquitously expressed HLA-C protein and a logic gate that enables tumour immunosurveillance. Thus, PreGame is a versatile tool that can accelerate our understanding of γδ T cell biology and facilitate the translation of γδ TCRs into universal therapeutics.

Author Info: (1) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. michael.stpaul@uhn.ca. (2) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. liam.hendrikse@uhn.ca. (3) Prin

Author Info: (1) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. michael.stpaul@uhn.ca. (2) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. liam.hendrikse@uhn.ca. (3) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (4) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (5) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Department of Computer Science and Mathematics, Faculty of Computer Science and Technology, Algoma University, Brampton, Ontario, Canada. (6) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (7) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (8) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (9) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (10) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (11) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (12) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (13) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (14) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (15) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (16) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (17) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (18) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (19) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (20) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (21) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (22) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (23) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (24) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (25) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (26) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (27) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (28) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (29) Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China. (30) Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China. (31) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (32) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (33) Queen Elizabeth II Health Sciences Centre, Dalhousie University, Halifax, Nova Scotia, Canada. (34) Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. (35) London Health Sciences Centre, London, Ontario, Canada. (36) CancerCare Manitoba, Winnipeg, Manitoba, Canada. (37) Cross Cancer Institute, Edmonton, Alberta, Canada. (38) Vancouver General Hospital, Vancouver, British Columbia, Canada. (39) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (40) Canadian Myeloma Research Group (CMRG), Vaughan, Ontario, Canada. (41) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (42) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (43) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (44) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (45) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. (46) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. suzanne.trudel@uhn.ca. (47) Princess Margaret Cancer Centre, Toronto, Ontario, Canada. tak.mak@uhn.ca. Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. tak.mak@uhn.ca. Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China. tak.mak@uhn.ca.

Characterization of circulating neoantigen-specific T cell responses and public T cell receptors shaping immune memory in Lynch syndrome carriers

Duzagac et al. characterized circulating neoantigen-specific T cells in Lynch syndrome (LS) carriers using functional assays, single-cell RNA/TCR sequencing, and integration with existing TCR datasets. Cancer-free LS carriers retained polyfunctional effector and tissue-surveillance states, whereas cancer survivors showed glycolytic, stress-associated, and exhausted T cell states. Recurrent frameshift neoantigens elicited strong T cell responses and tumor organoid killing. Public neoantigen-specific TCRs showed clonal expansion and were shared across blood, pre-cancer, and tumor tissues. Reconstructed TCRs mediated HLA-restricted tumor recognition.

Contributed by Shishir Pant

Duzagac et al. characterized circulating neoantigen-specific T cells in Lynch syndrome (LS) carriers using functional assays, single-cell RNA/TCR sequencing, and integration with existing TCR datasets. Cancer-free LS carriers retained polyfunctional effector and tissue-surveillance states, whereas cancer survivors showed glycolytic, stress-associated, and exhausted T cell states. Recurrent frameshift neoantigens elicited strong T cell responses and tumor organoid killing. Public neoantigen-specific TCRs showed clonal expansion and were shared across blood, pre-cancer, and tumor tissues. Reconstructed TCRs mediated HLA-restricted tumor recognition.

Contributed by Shishir Pant

ABSTRACT: Lynch syndrome (LS), a common inherited genetic condition predisposing to cancer, provides a unique model to study immune surveillance at the earliest stages of tumorigenesis. A hallmark of LS carcinogenesis is the generation of highly immunogenic neoantigens, yet the transcriptomic states of the T cells that recognize them remain poorly understood. Here, we characterize neoantigen-specific T cells from LS carriers using functional assays, single-cell RNA/T cell receptor (TCR) sequencing, and repertoire integration with existing large datasets. Recurrent neoantigens elicit strong immune responses, with neoantigen-specific T cells mediating cytotoxicity against tumor organoids. Single-cell analysis reveals oligoclonal expansions spanning effector and memory states with enrichment for exhausted subsets among cancer survivors and retention of polyfunctional effectors in cancer-free carriers. Cross-cohort analysis identifies public TCR clonotypes in circulation that overlap with pre-cancer and tumor tissue repertoires. Together, these findings define the architecture of circulating neoantigen-specific immune memory in LS and highlight public TCRs as candidates for immune monitoring and immunoprevention.

Author Info: (1) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Clinical Cancer Prevention, The University of T

Author Info: (1) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (3) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (4) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (5) Institute of Immunology, Medical University of Vienna, Vienna, Austria. (6) Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (7) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (8) Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: evilar@mdanderson.org.

Cancers modulate processing and presentation of p53 neoantigens to evade T cell detection Spotlight 

Haratani et al. found that most neoantigens arising from truncal mutations in TP53 were not presented, either because they arose from processing-resistant regions of p53 or because the required HLA allele was absent. Presentable antigens that were immunologically distinct could also be masked in tumors via increased activity of the aminopeptidase ERAP1, which prevented the display of a high-affinity HLA-A*02:01 complex containing a strongly immunogenic 11-mer. Instead, tumors favored the display of low-affinity complexes with a poorly immunogenic 9-mer, resulting in only weak T cell-mediated antitumor immunity, despite high-quality TCRs.

Contributed by Lauren Hitchings

Haratani et al. found that most neoantigens arising from truncal mutations in TP53 were not presented, either because they arose from processing-resistant regions of p53 or because the required HLA allele was absent. Presentable antigens that were immunologically distinct could also be masked in tumors via increased activity of the aminopeptidase ERAP1, which prevented the display of a high-affinity HLA-A*02:01 complex containing a strongly immunogenic 11-mer. Instead, tumors favored the display of low-affinity complexes with a poorly immunogenic 9-mer, resulting in only weak T cell-mediated antitumor immunity, despite high-quality TCRs.

Contributed by Lauren Hitchings

ABSTRACT: TP53 mutations occur early in malignant transformation as truncal events in tumor evolution and are therefore generally present in all descendant tumor cells, creating an immunological vulnerability. Here, we examined the immunogenicity and antigenicity of p53 neoantigens emerging from these truncal mutations. Comprehensive immunopeptidomics revealed that hotspot mutations in human tumors preferentially localize to p53 regions resistant to antigen processing, thereby avoiding display altogether. Moreover, for neoantigens presentable by HLA-A∗02:01 or HLA-B∗07:02 and structurally divergent from corresponding wild-type p53 peptide-HLA complexes, clinical tumors commonly lacked the relevant presenting HLA allele. Tumor cells further resisted T cell killing through increased activity of the aminopeptidase ERAP1, preventing display of high-affinity HLA-A∗02:01 complexes containing an immunogenic p53I195F-derived 11-mer, or by expressing low-affinity HLA-A∗02:01 complexes containing a p53R175H-derived 9-mer with poor antigenicity despite high-quality human TCRs. These findings define mechanisms by which tumors restrict targetable truncal neoantigen display and suggest immunopeptidome shift strategies to circumvent immune escape.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. (2) Department of Medical Oncolo

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. (2) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, MA, USA. (3) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, MA, USA. (4) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. (5) Structural Biology Center, X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA. (6) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Dermatology, Harvard Medical School, Boston, MA, USA. (7) Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. (8) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. (9) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (10) Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA. (11) Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA. (12) Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA. (13) Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA. (14) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (15) Department of Pathology, Boston Children's Hospital, Boston, MA, USA. (16) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (17) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (18) Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA. (19) Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA. (20) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA. (21) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (22) Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA. (23) Department of Pathology, Boston Children's Hospital, Boston, MA, USA; Department of Molecular Biotechnology and Health Sciences, University of Torino, 10125 Torino, Italy; Division of Hematopathology, IEO European Institute of Oncology IRCCS, Milan, Italy. (24) Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. (25) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: david_barbie@dfci.harvard.edu. (26) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: ellis_reinherz@dfci.harvard.edu.

A serpin-myeloid axis in pancreatic cancer heterogeneity and immune evasion Spotlight 

Falcomatà et al. used perturb-map spatial functional genomics to identify tumor-derived extracellular factors that promote PDAC immune evasion. SERPINE1 (encoding PAI1) and SERPINB2 promoted fibrin-rich ECM niches that retained and polarized macrophages toward immunosuppressive states while excluding cytotoxic CD8+ T cells. Loss of serpins or pharmacologic inhibition of PAI1 improved tumor control and sensitized orthotopic KPC PDAC tumors to anti-PD-1. In human PDACs, SERPINB1/2-expressing tumor cells were embedded within immunosuppressive niches enriched with SPP1+MARCO+ macrophages.

Contributed by Shishir Pant

Falcomatà et al. used perturb-map spatial functional genomics to identify tumor-derived extracellular factors that promote PDAC immune evasion. SERPINE1 (encoding PAI1) and SERPINB2 promoted fibrin-rich ECM niches that retained and polarized macrophages toward immunosuppressive states while excluding cytotoxic CD8+ T cells. Loss of serpins or pharmacologic inhibition of PAI1 improved tumor control and sensitized orthotopic KPC PDAC tumors to anti-PD-1. In human PDACs, SERPINB1/2-expressing tumor cells were embedded within immunosuppressive niches enriched with SPP1+MARCO+ macrophages.

Contributed by Shishir Pant

ABSTRACT: Pancreatic ductal carcinoma (PDAC) is characterized by a highly immunosuppressive, extracellular matrix-rich microenvironment, yet tumours display marked heterogeneity(1-4). This raises the question of whether immune resistance is a global tumour property or is organized within spatially restricted niches. Here, using Perturb-map spatial functional genomics, we determine how different genes shape the growth and cellular environments of PDAC clones across space and time. This analysis revealed early gene-driven remodelling of local immune neighbourhoods preceding late-stage spatial clonal dominance. We identify SERPINE1 (encoding plasminogen activator inhibitor 1 (PAI1)) and SERPINB2 (encoding PAI2) as dominant regulators of tumour microenvironment control and immune evasion. These serpins promote stabilization of fibrin-rich extracellular matrix niches that spatially retain and programme macrophages towards immunosuppressive states while excluding cytotoxic T cells. Loss of Serpine1 or Serpinb2, or pharmacological inhibition of PAI1 or CD18, improves tumour control in mice and synergizes with anti-PD-1. Multimodal spatial analysis of patient tumours revealed that immunosuppressive niches form around rare SERPINB2- and SERPINE1-expressing PDAC subpopulations, dominated by SPP1+/MARCO+ macrophages. These findings identify cancer-derived SERPINE1 and SERPINB2 as local spatial organizers of immune suppression, linking tumour-intrinsic heterogeneity to local microenvironmental control and immunotherapy resistance in PDAC.

Author Info: (1) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Author Info: (1) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (2) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (3) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (4) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (5) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (6) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (7) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (8) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (9) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (10) Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (11) Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (12) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (13) Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. brian.brown@mssm.edu. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. brian.brown@mssm.edu. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. brian.brown@mssm.edu. Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. brian.brown@mssm.edu.

Intratumoral T cell activation kills tumors regardless of T cell specificity Spotlight 

Smith, Dao, and Gavil et al. showed that activation of unexhausted non-tumor-specific memory T cells in the TIME induced tumor clearance in the absence of NK cell- or TCRαβ-dependent tumor cell recognition, although the latter was required to protect from dLN metastases after primary tumor surgical resection. Expression of IFNγ, TNF, and NO by T and myeloid cells, dependent on endothelial cell VCAM-1 upregulation, induced caspase-dependent apoptosis of primary tumor cells, which was boosted by anti-PD-L1. Gene expression analyses indicated that these antitumor mechanisms also occurred in human patients with melanoma with favorable prognoses.

Contributed by Paula Hochman

Smith, Dao, and Gavil et al. showed that activation of unexhausted non-tumor-specific memory T cells in the TIME induced tumor clearance in the absence of NK cell- or TCRαβ-dependent tumor cell recognition, although the latter was required to protect from dLN metastases after primary tumor surgical resection. Expression of IFNγ, TNF, and NO by T and myeloid cells, dependent on endothelial cell VCAM-1 upregulation, induced caspase-dependent apoptosis of primary tumor cells, which was boosted by anti-PD-L1. Gene expression analyses indicated that these antitumor mechanisms also occurred in human patients with melanoma with favorable prognoses.

Contributed by Paula Hochman

ABSTRACT: Immunotherapies putatively require tumor-specific T cells. Here we show how T cells can eliminate tumors without tumor specificity via paracrine signaling. Activating unexhausted bystander non-tumor-specific T cells within tumors resulted in tumor elimination without conventional recognition-dependent mechanisms and in the absence of any tumor-specific T cell receptor (TCR)αβ+ T cells. Robust T cell activation recruited immune cells, used innate leukocytes and triggered a tumoricidal combination of effector molecules and panoptotic pathways. Mechanistically, interferon-γ, tumor necrosis factor and nitric oxide induced caspase-dependent death, recapitulating melanoma clearance in mice or human melanoma cell death in vitro. Gene expression signatures associated with this response in mice were predictive of survival among human patients with melanoma. Thus, triggering productive T cell activation within tumors can be sufficient for immunotherapy, without needing to induce or rescue cancer-specific responses.

Author Info: (1) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapo

Author Info: (1) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (2) Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (3) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (4) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (5) Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (6) Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Biology and Program in Biochemistry, Bowdoin College, Brunswick, ME, USA. (7) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (8) Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (9) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (10) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (11) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (12) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (13) Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Dartmouth Cancer Center, Lebanon, NH, USA. (14) Department of Pathology, University of California San Francisco, San Francisco, CA, USA. (15) Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MN, USA. (16) Department of Medicine, University of Minnesota, Minneapolis, MN, USA. (17) Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA. (18) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (19) Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (20) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. masopust@umn.edu. Center for Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. masopust@umn.edu.

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

Spotlight 

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

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

Tumor immune microenvironment remodeling predicts response to checkpoint inhibitor therapy Spotlight 

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

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.

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

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

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.

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

Spotlight 

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

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.

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