Journal Articles

Loss of the autoimmune risk gene TREX1 reveals a convergence of mechanisms promoting immune tolerance loss and antitumor immunity Spotlight 

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

ABSTRACT: Checkpoint inhibitors targeting PD-1 and CTLA-4 have transformed cancer therapy. Both are genetically associated with autoimmune disorders. Moreover, certain immune-related adverse events and autoimmune risk variants are linked to the clinical efficacy of checkpoint inhibition. These associations suggest common principles governing successful cancer immunotherapy and autoimmune susceptibility. Here, we show that ablation of the cytosolic DNA exonuclease TREX1 predisposes mice to autoimmunity while promoting robust antitumor immunity. Constitutive TREX1 loss leads to early onset autoimmunity, characterized by multiorgan CD8+ T cell infiltration, myocarditis, and Sjgren's syndrome-like disease. In contrast, induced systemic TREX1 ablation is well tolerated and promotes effective CD8+ T cell-driven antitumor immunity. Detailed phenotypic studies revealed a notable overlap between productive antitumor and pathogenic autoimmune CD8+ T cell responses. Collectively, we provide mechanistic evidence for interrelated mechanisms underlying autoimmunity and successful cancer immunotherapy, uncover key parallels between adaptive T cell and innate immune checkpoints, and suggest that targeting autoimmune risk genes represents a promising future avenue for cancer immunotherapy.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Tumors hijack immune-privileging regulons via distinct cell types to confer T cell desertion and immunotherapy resistance across various cancers Spotlight 

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

ABSTRACT: Immune checkpoint blockade (ICB) has transformed oncology, yet most patients fail to respond, suffer from hyper-progressive disease, or face severe immune-related toxicities, underscoring the urgent need for biomarkers that identify non-responders. Here we show that tumors co-opt an immune-privileging regulon signature (IMPREG) mirroring transcriptional programs of immune-privileged organs - to enforce T-cell desertion and ICB resistance across solid tumor types. Single-cell and spatial transcriptomic analyses reveal that tumors activate IMPREG through three distinct cellular routes: malignant cells adopting immature neuronal states, cancer-associated fibroblasts assuming myofibroblast identities, or endothelial cells - each creating localized niches of immune suppression and antigen-presentation collapse. Across 4 discovery and 36 validation clinical datasets, IMPREG consistently predicts immunotherapy resistance in 14 distinct cancer types, functioning as an orthogonal marker independent of established biomarkers. Crucially, IMPREG-expressing tumors show enhanced sensitivity to EGFR inhibitors or anti-angiogenic therapies in specific tumor entities. These findings suggest IMPREG as a dual-utility predictive biomarker for personalized treatment stratification.

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC,

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA. 4UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu. 5Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu.

CD39+CD49a+CD103+ cytotoxic tissue-resident natural killer cells infiltrate and control solid epithelial tumor growth in mice

Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Human tissue-resident natural killer (NK) cells (trNK cells), broadly defined by markers of tissue residency, such as CD49a [integrin α1 (ITGA1)] and CD103 [integrin αE (ITGAE)], are increasingly recognized for their immunoregulatory role in host control of infection, malignancy, and autoimmunity. Although the importance of transforming growth factor-β in trNK cell differentiation has been demonstrated, the context in which the differentiation of CD49a+CD103+ trNK cells occurs can result in either an immunosuppressive phenotype (e.g., decidual NK cells) or a highly cytotoxic one (e.g., some tumor trNK subsets). To understand this dichotomy better, we used a multiomic approach to molecularly characterize these cells. We identified a cytotoxic trNK (ctrNK) cell population, characterized by the expression of CD39. These ctrNK cells exhibited superior cytolytic activity against tumor target cells, enhanced capacity to infiltrate into solid tumor microenvironments, and augmented ability to control solid tumor growth in vivo compared with conventionally activated peripheral NK cells. This heightened cytolytic and infiltrative functionality of ctrNK cells appeared to be conferred, in part, by the expression of CD103 and by avidity for tumor targets. Because adoptive immune cell therapy of solid tumor malignancies has been challenged by the inefficiency of ex vivo expanded immune cells to infiltrate immunosuppressive solid tumor microenvironments, our observations that ctrNK cells can be differentiated and expanded ex vivo present a potential platform for adoptive cell therapy of solid tumor malignancies.

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanfo

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. 3Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA. 4Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. 5Pelotonia Institute for Immuno-Oncology, Ohio State University Comprehensive Cancer Center-the James, Columbus, OH 43210, USA. 6Department of Molecular Medicine and Therapeutics, College of Medicine, Ohio State University, Columbus, OH 43210, USA. 7Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. 8Division of Infectious Diseases, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. 9Section of Computational Biology, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. 10Siteman Cancer Center at WashU Medicine, St. Louis, MO 63110, USA.

Integrated Single-Cell Profiling Reveals Dichotomous NK Cell Populations Associated with Immunosuppression in Solid Tumors Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Natural killer (NK) cells represent key effectors of antitumor immunity, yet emerging evidence highlights populations with distinct roles in cancer. Despite such expanded diversity within the NK cell repertoire, we lack an understanding of how this heterogeneity impacts immune responses and downstream clinical outcomes. Using single-cell RNA-sequencing (scRNA-seq), we systematically profiled NK cells across cancer and uncovered a dichotomous phenotypic and functional landscape of tumor-infiltrating NK cells shaped by opposing intrinsic signaling programs that drive the expression of IFNG or TGFB1. These divergent programs are associated with distinct transcription factor circuits that integrate cues within the tumor microenvironment and skew NK cells towards pro-inflammatory or suppressive functions. We found that the capacity for NK cells to engage in either functional direction is intrinsically linked to their phenotypic identity. Canonical NK cells recruited from circulation predominantly directed suppressive TGFB1 signals towards effector CD8+ T cells in tumors. Of note, these subsets exhibited higher TGFB1 expression than intratumoral myeloid cells across tumor types. In contrast, a tissue-resident adaptive subset exhibited exclusively pro-inflammatory IFNG-driven profiles and was associated with prolonged survival in both primary and metastatic tumor settings. Moreover, these tissue-resident adaptive NK cells, but not other subsets, were linked to response to immune checkpoint blockade. Collectively, our study reveals a previously unrecognized regulatory axis in NK cells that shapes NK cell diversity and augments broader antitumor immune responses.

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United Stat

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United States. 4Caris Life Sciences (United States) Phoenix, AZ United States. 5University of Minnesota Minnesota, MN United States. 6University of Minnesota Minneapolis, Minnesota United States. 7Mayo Clinic Rochester, MN United States. 8University of Chicago Chicago, IL United States. 9Caris Life Sciences (United States) Los Angeles, CA United States. 10University of Minnesota Minneapolis, MN United States.

Time-of-day of first checkpoint inhibitor dose influences clinical outcomes and immune responses in hepatocellular carcinoma Spotlight 

Among a retrospective cohort of 84 HCC patients treated with ICB, those who received their first ICB dose in the morning (prior to 12 noon) had increased PFS (and a trend in OS) compared to those receiving a first dose in the afternoon. The timing of subsequent doses did not have a similar stratifying effect, and morning dosing did not raise the rate of irAEs. Comparing baseline and early on-treatment blood samples, Li et al. found that patients first receiving ICB in the morning had diminished induction of certain cytokines (IL-6, IL-1B, VEGF-A, and IL-21) and a greater expansion of cytotoxic CD8+ Tcm cells, compared to those receiving an afternoon dose.

Contributed by Alex Najibi

Among a retrospective cohort of 84 HCC patients treated with ICB, those who received their first ICB dose in the morning (prior to 12 noon) had increased PFS (and a trend in OS) compared to those receiving a first dose in the afternoon. The timing of subsequent doses did not have a similar stratifying effect, and morning dosing did not raise the rate of irAEs. Comparing baseline and early on-treatment blood samples, Li et al. found that patients first receiving ICB in the morning had diminished induction of certain cytokines (IL-6, IL-1B, VEGF-A, and IL-21) and a greater expansion of cytotoxic CD8+ Tcm cells, compared to those receiving an afternoon dose.

Contributed by Alex Najibi

BACKGROUND: Although immune checkpoint inhibitors (ICIs) have long half-lives, preclinical and retrospective clinical studies across multiple tumor types suggest that the time-of-day of ICI infusion may influence therapeutic efficacy by aligning initial drug exposure with circadian peaks in T-cell responsiveness. The immunological basis of this phenomenon and its clinical relevance in hepatocellular carcinoma (HCC) remains unknown. METHODS: We followed patients with advanced HCC receiving ICI therapy at Johns Hopkins from 2021 to 2025, classifying them into a morning (first treatment before 12:00 hours) or afternoon (first treatment after 12:00 hours) group. We assessed clinical outcomes and compared immunological responses from baseline to early-on-treatment by profiling peripheral blood mononuclear cells using cytometry by time-of-flight and plasma cytokines using a 39-plex Luminex assay. RESULTS: Our cohort included 84 patients, 39 of whom received their first infusion in the morning. There were no statistically significant differences in baseline demographic or clinical characteristics between patients initiating therapy in the morning versus afternoon. The morning group had superior progression-free survival (multivariable HR 0.50, 95% CI 0.30 to 0.84, p<0.01) and higher odds of treatment response (multivariable OR 3.26, 95% CI 1.08 to 10.90, p<0.05), with no significant increase in immune-related adverse events. The timing of subsequent infusions after the first dose had no impact on outcomes. Immunological responses diverged after the initial dose, with morning-treated patients showing reduced interleukin (IL)-6 levels (p<0.01) and greater expansion of cytotoxic central memory CD8+ T_cells (p=0.01) as well as cytotoxic effector and effector memory CD8+ T_cells (p=0.06). CONCLUSIONS: Morning first-dose infusion of ICIs in HCC was associated with improved clinical outcomes and distinct immune responses, including reduced IL-6 signaling and expansion of cytotoxic central memory CD8+ T cells. These findings suggest that the timing of the initial infusion can imprint an immunological program that shapes subsequent antitumor immunity, providing a mechanistic rationale for strategically scheduling ICI administration.

Author Info: (1) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (2) Sidney

Author Info: (1) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (2) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (3) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (4) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (5) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (6) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (7) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (8) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (9) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (10) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (11) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (12) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (13) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (14) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (15) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (16) F Hoffmann-La Roche Ltd, Basel, Switzerland. (17) F Hoffmann-La Roche Ltd, Basel, Switzerland. Genentech Inc, South San Francisco, California, USA. (18) Genentech Inc, South San Francisco, California, USA. (19) Genentech Inc, South San Francisco, California, USA. (20) Genentech Inc, South San Francisco, California, USA. (21) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Ludwig Institute for Cancer Research, Baltimore, Maryland, USA. (22) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (23) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (24) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (25) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (26) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (27) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (28) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA mark.yarchoan@jhmi.edu mnakaza2@jhmi.edu. (29) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA mark.yarchoan@jhmi.edu mnakaza2@jhmi.edu.

Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression Spotlight 

Using single-cell and spatial transcriptomics in human and rat models, Bui et al. mapped immune remodeling of normal breast, pre-malignant (DCIS) , and invasive (IBC) breast cancer and identified a proliferative FOXP3int MKI67hi cycling Treg (cycTreg) subset. CycTregs emerged at the DCIS-IBC junction, expanded in IBC, and predicted CD8+ infiltration, TCR diversity, disease-specific survival, and DCIS recurrence. CycTreg abundance correlated with CLEC10A+ cDC2s, high HLA class II, and IL-33-producing CAFs. OX40 agonism plus anti-PD-L1 or IL-33 blockade reduced cycTreg, remodeled CAF states, and restored antitumor immunosurveillance.

Contributed by Shishir Pant

Using single-cell and spatial transcriptomics in human and rat models, Bui et al. mapped immune remodeling of normal breast, pre-malignant (DCIS) , and invasive (IBC) breast cancer and identified a proliferative FOXP3int MKI67hi cycling Treg (cycTreg) subset. CycTregs emerged at the DCIS-IBC junction, expanded in IBC, and predicted CD8+ infiltration, TCR diversity, disease-specific survival, and DCIS recurrence. CycTreg abundance correlated with CLEC10A+ cDC2s, high HLA class II, and IL-33-producing CAFs. OX40 agonism plus anti-PD-L1 or IL-33 blockade reduced cycTreg, remodeled CAF states, and restored antitumor immunosurveillance.

Contributed by Shishir Pant

ABSTRACT: Immune escape during the ductal carcinoma in situ (DCIS)-to-invasive breast cancer (IBC) transition shapes tumor evolution. Through transcriptomic mapping of the immune landscapes of normal breast, DCIS, and IBC from large patient cohorts, we identified T and myeloid cells as the primary distinguishing features between DCIS and IBC. We discovered cycling regulatory T cells (cycTreg) as an orchestrator of immunosuppression in IBC. cycTreg frequency predicts cytotoxic CD8(+), TCR diversity, disease-specific survival in IBC, and recurrence in DCIS. In a rat model of breast cancer, we demonstrated that cycTreg act as precursors to mature Treg and are inducible by tumor-localized type 2 dendritic cells. Profiling of tumors subjected to OX40 and PD-L1 therapies revealed an IL-33-mediated fibroblast-cycTreg signaling loop, the disruption of which enhances intratumoral antigen-experienced CD8(+) effectors and systemic immunosurveillance. Our study defines cycTreg as critical inducers of immune escape and promising immuno-oncology targets in breast cancer.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (2) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (3) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (4) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (5) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (6) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (7) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (8) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (9) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (10) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (11) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (12) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (13) Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27705, USA. (14) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (15) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (16) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA. (17) Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA. (18) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (19) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (20) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (21) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (22) Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA. (23) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (24) Department of Surgery, Duke University School of Medicine, Durham, NC 27708, USA. (25) Department of Surgery, Washington University School of Medicine, St. Louis, MO 63108, USA. (26) Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea. (27) Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA. (28) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (29) UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA. (30) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (31) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (32) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (33) Department of Surgery, Duke University School of Medicine, Durham, NC 27708, USA. (34) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (35) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: kornelia_polyak@dfci.harvard.edu.

Immune-induced TCR-like antibodies regulate specific T cell response in mice Spotlight 

Kishida et al. showed that immune-induced TCR-like antibodies (iTabs) – antibodies that are specific to an antigen peptide–MHC-II complex – were produced during helper T cell responses to immunization with various antigens. These iTabs induced antigen-dependent depletion of target cells, blocked TCR recognition of specific peptide–MHC-II complexes, and prevented activation of antigen-specific T cells, but only when the presented peptides contained specific flanking residues. In a mouse model, treatment with iTabs or immunization with a peptide that induced iTabs effectively limited the development of autoimmune encephalomyelitis.

Contributed by Lauren Hitchings

Kishida et al. showed that immune-induced TCR-like antibodies (iTabs) – antibodies that are specific to an antigen peptide–MHC-II complex – were produced during helper T cell responses to immunization with various antigens. These iTabs induced antigen-dependent depletion of target cells, blocked TCR recognition of specific peptide–MHC-II complexes, and prevented activation of antigen-specific T cells, but only when the presented peptides contained specific flanking residues. In a mouse model, treatment with iTabs or immunization with a peptide that induced iTabs effectively limited the development of autoimmune encephalomyelitis.

Contributed by Lauren Hitchings

ABSTRACT: Antigen-specific regulation of T cell response is crucial for limiting hyperimmune response. However, the molecular mechanisms governing specific immune regulation remain unclear. In this study, we discover that antibodies specific to the antigen peptide-MHC class II complex are produced during helper T cell responses to various antigens, including hen egg lysozyme and proteolipid protein peptide. These antibodies specifically inhibit T cell receptor (TCR) recognition of MHC class II molecules presenting specific antigen peptide. We term these antibodies 'immune-induced TCR-like antibodies' or iTabs. Immunization with peptides containing flanking residues induces iTabs whereas immunization with peptides lacking flanking residues does not. Furthermore, we show that immunization with iTab-inducible peptide or iTab treatment suppress autoimmune disease development in a mouse model of experimental autoimmune encephalomyelitis. Thus, our findings provide a strategy for suppressing antigen-specific helper T cell responses using specific peptides, potentially controlling autoimmune diseases.

Author Info: (1) Department of Immunochemistry, Research Institute for Microbial Diseases, The University of Osaka, Suita, Osaka, Japan. (2) Biostructural Mechanism Group, RIKEN SPring-8 Center

Author Info: (1) Department of Immunochemistry, Research Institute for Microbial Diseases, The University of Osaka, Suita, Osaka, Japan. (2) Biostructural Mechanism Group, RIKEN SPring-8 Center, Hyogo, Japan. (3) Department of Drug Target Protein Research, Shinshu University School of Medicine, Matsumoto, Nagano, Japan. Department of Structural Biology and Biochemistry, Institute of New Industry Incubation, Institute of Science Tokyo, Tokyo, Japan. (4) Department of Immunochemistry, Research Institute for Microbial Diseases, The University of Osaka, Suita, Osaka, Japan. Laboratory for Innate Immune Systems, Department of Microbiology and Immunology, Graduate School of Medicine, The University of Osaka, Suita, Osaka, Japan. (5) Biostructural Mechanism Group, RIKEN SPring-8 Center, Hyogo, Japan. Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi, Japan. (6) Department of Drug Target Protein Research, Shinshu University School of Medicine, Matsumoto, Nagano, Japan. Department of Structural Biology and Biochemistry, Institute of New Industry Incubation, Institute of Science Tokyo, Tokyo, Japan. (7) Department of Immunochemistry, Research Institute for Microbial Diseases, The University of Osaka, Suita, Osaka, Japan. arase@biken.osaka-u.ac.jp. Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, The University of Osaka, Suita, Osaka, Japan. arase@biken.osaka-u.ac.jp. Center for Advanced Modalities and DDS, The University of Osaka, Suita, Osaka, Japan. arase@biken.osaka-u.ac.jp. Center for Infectious Disease Education and Research, The University of Osaka, Suita, Osaka, Japan. arase@biken.osaka-u.ac.jp.

PD-1 antibody-bound progenitor-exhausted CD8+ T cells in lymph nodes boost PD-1-blockade anti-tumor immunity in gastrointestinal cancer

Spotlight 

Utilizing scRNA/TCRseq, CITEseq, and a novel assay for cell-bound anti-PD-1 to study the dynamics of T cells targeted by anti-PD-1, Nose and Yasumizu et al. first found that abundance of progenitor-exhausted CD8+ T cells (Tpex) in metastasis-free lymph nodes (LNs), but not tumors or metastatic LNs, correlated with better prognosis in patients with anti-PD-1-naive gastric cancer. Anti-PD-1 promoted the proliferation of anti-PD-1high-bound Tpex in LNs, and clonotypes overlapped with intratumoral anti-PD-1-bound exhausted T cells (Tex), suggesting that anti-PD-1high-bound Tpex migrate to the tumor, where they differentiate into Tex.

Contributed by Ute Burkhardt

Utilizing scRNA/TCRseq, CITEseq, and a novel assay for cell-bound anti-PD-1 to study the dynamics of T cells targeted by anti-PD-1, Nose and Yasumizu et al. first found that abundance of progenitor-exhausted CD8+ T cells (Tpex) in metastasis-free lymph nodes (LNs), but not tumors or metastatic LNs, correlated with better prognosis in patients with anti-PD-1-naive gastric cancer. Anti-PD-1 promoted the proliferation of anti-PD-1high-bound Tpex in LNs, and clonotypes overlapped with intratumoral anti-PD-1-bound exhausted T cells (Tex), suggesting that anti-PD-1high-bound Tpex migrate to the tumor, where they differentiate into Tex.

Contributed by Ute Burkhardt

ABSTRACT: While progenitor-exhausted T cells (Tpex) expressing TCF1 and PD-1 are crucial for the therapeutic effect of immune checkpoint inhibitors (ICIs) with therapeutic anti-PD-1 antibodies (aPD-1), the dynamics of ICI-bound Tpex are not fully understood. In this study, we investigate ICI-bound T cells in detail using combined sequencing analysis at the single-cell level. By analyzing samples from gastrointestinal cancer patients with or without ICI treatment, we find that Tpex are enriched in proximal lymph nodes (LNs) and proliferate at a high rate after ICI treatment. Importantly, aPD-1 high-bound Tpex in LNs share T-cell receptor clonotypes with intratumoral exhausted CD8(+) T cells (Tex), suggesting their migration to tumor sites after ICI treatment. This study thus provides new insights into how ICIs enhance anti-tumor immunity by acting on Tpex in LNs, deepening our understanding of the cellular mechanisms underlying ICI therapy.

Author Info: (1) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. Department of Clinical Research in Tumor Immunology, Graduate Sch

Author Info: (1) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. (2) Experimental Immunology, WPI Immunology Frontier Research Center, The University of Osaka, Suita, Japan. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), The University of Osaka, Suita, Japan. (3) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. tsaito@gesurg.med.osaka-u.ac.jp. Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. tsaito@gesurg.med.osaka-u.ac.jp. (4) Experimental Immunology, WPI Immunology Frontier Research Center, The University of Osaka, Suita, Japan. (5) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. (6) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. (7) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (8) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (9) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (10) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (11) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (12) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (13) Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. Pharmaceutical Research Division, Shionogi & Co., Ltd., Toyonaka, Japan. (14) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (15) Department of Pathology, Institute of Medical Science (Medical Research Center), Tokyo Medical University, Tokyo, Japan. (16) Experimental Immunology, WPI Immunology Frontier Research Center, The University of Osaka, Suita, Japan. Department of Basic Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Osaka, Japan. (17) Experimental Immunology, WPI Immunology Frontier Research Center, The University of Osaka, Suita, Japan. (18) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, The University of Osaka, Suita, Japan. (19) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan. (20) Department of Gastroenterological Surgery, Graduate School of Medicine, The University of Osaka, Suita, Japan.

Acute and chronic infections drive distinct trajectories in human memory CD4+ T cell formation

Spotlight 

Comparing CD4+ T cells generated during acute or chronic hepatitis C virus (HCV) infection, Reinscheid and Weisser et al. evaluated patient samples and found that acute infection generated various subsets of progenitor CD4+ T cells, including subsets also observed in chronic infection. In chronic infection, a subset of stem-like/resting Bcl-2+ CD4+ T cells likely gave rise to a subset of T-bet+ effector CD4+ T cells. In patients treated with DAA to clear the virus, the effector subset essentially disappeared, while the stem-like subset formed a functional long-term memory pool that was distinct from the memory pools that formed after spontaneous HCV clearance.

Contributed by Lauren Hitchings

Comparing CD4+ T cells generated during acute or chronic hepatitis C virus (HCV) infection, Reinscheid and Weisser et al. evaluated patient samples and found that acute infection generated various subsets of progenitor CD4+ T cells, including subsets also observed in chronic infection. In chronic infection, a subset of stem-like/resting Bcl-2+ CD4+ T cells likely gave rise to a subset of T-bet+ effector CD4+ T cells. In patients treated with DAA to clear the virus, the effector subset essentially disappeared, while the stem-like subset formed a functional long-term memory pool that was distinct from the memory pools that formed after spontaneous HCV clearance.

Contributed by Lauren Hitchings

ABSTRACT: Virus-specific CD4(+) T cells are essential for coordinating adaptive immunity during infection, but their differentiation and maintenance in chronic infection remain unclear. Using human hepatitis C virus (HCV) infection as a model, we assessed the determinants of virus-specific CD4(+) T cell immunity in acute, spontaneously cleared, chronic, and therapeutically cured infections. During acute infection, multiple subsets of progenitor CD4(+) T cells emerged, including subsets that are also found in chronic infection. In chronic infection, stem-like Bcl-2(+) CD4(+) T cells and T-bet(+) effector CD4(+) T cells existed in a progenitor/progeny relationship. Following therapy-mediated HCV cure, these cells retained their chronic signature but formed a stable memory pool that persisted for years and was distinct from HCV-specific CD4(+) T cell memory after spontaneous clearance. Collectively, our findings highlight differences in CD4(+) T cell fates that depend on infection outcomes and reveal common principles of CD4(+) and exhausted CD8(+) T cell maintenance during and after chronic infection.

Author Info: (1) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany

Author Info: (1) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany. (2) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany. (3) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. (4) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. (5) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. (6) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany. (7) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. (8) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany. (9) Institute for Transfusion Medicine and Gene Therapy, Freiburg University Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany. (10) Institute of Virology, Medical Faculty and University Hospital DŸsseldorf, Heinrich Heine University DŸsseldorf, DŸsseldorf, Germany. (11) Institute of Virology, Medical Faculty and University Hospital DŸsseldorf, Heinrich Heine University DŸsseldorf, DŸsseldorf, Germany. (12) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany. (13) Institute of Virology, Medical Faculty and University Hospital DŸsseldorf, Heinrich Heine University DŸsseldorf, DŸsseldorf, Germany. (14) Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland. (15) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany, partner site Freiburg, Freiburg, Germany; Signaling Research Centers BIOSS and CIBSS, Freiburg, Germany. (16) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. (17) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. Electronic address: robert.thimme@uniklinik-freiburg.de. (18) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. Electronic address: tobias.boettler@uniklinik-freiburg.de. (19) Department of Medicine II, Medical Center - University of Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany. Electronic address: maike.hofmann@uniklinik-freiburg.de.

Precancerous niche remodelling dictates nascent tumour persistence Spotlight 

Using the DEN carcinogenesis model, Skrupskelyte and Arias et al. showed that early tumor persistence depended on the formation of a fibrotic precancerous niche. Stress responses in nascent epithelial lesions activated EGFR signaling, induced SOX9⁺, recruited PDGFRαlow fibroblasts, and drove the formation of a fibronectin (FN1)-rich niche that promoted tumor growth. Tumor-derived stroma alone was sufficient to impose tumor traits in normal epithelium. Inhibition of either fibronectin fibrillogenesis or EGFR signaling prevented niche formation and reduced tumor burdens. Heterogeneous AREG+ (an EGF ligand) and/or SOX9+ populations were present in early human oesophageal carcinoma.

Contributed by Shishir Pant

Using the DEN carcinogenesis model, Skrupskelyte and Arias et al. showed that early tumor persistence depended on the formation of a fibrotic precancerous niche. Stress responses in nascent epithelial lesions activated EGFR signaling, induced SOX9⁺, recruited PDGFRαlow fibroblasts, and drove the formation of a fibronectin (FN1)-rich niche that promoted tumor growth. Tumor-derived stroma alone was sufficient to impose tumor traits in normal epithelium. Inhibition of either fibronectin fibrillogenesis or EGFR signaling prevented niche formation and reduced tumor burdens. Heterogeneous AREG+ (an EGF ligand) and/or SOX9+ populations were present in early human oesophageal carcinoma.

Contributed by Shishir Pant

ABSTRACT: Interactions between mutant cells and their environment have a key role in determining cancer susceptibility(1-3). However, understanding of how the precancerous microenvironment contributes to early tumorigenesis remains limited. Here we show that newly emerging tumours at their most incipient stages shape their microenvironment in a critical process that determines their survival. Analysis of nascent squamous tumours in the upper gastrointestinal tract of the mouse reveals that the stress response of early tumour cells instructs the underlying mesenchyme to form a supportive 'precancerous niche', which dictates the long-term outcome of epithelial lesions. Stimulated fibroblasts beneath emerging tumours activate a wound-healing response that triggers a marked remodelling of the underlying extracellular matrix, resulting in the formation of a fibronectin-rich stromal scaffold that promotes tumour growth. Functional heterotypic 3D culture assays and in vivo grafting experiments, combining carcinogen-free healthy epithelium and tumour-derived stroma, demonstrate that the precancerous niche alone is sufficient to confer tumour properties to normal epithelial cells. We propose a model in which both mutations and the stromal response to genetic stress together define the likelihood of early tumours to persist and progress towards more advanced disease stages.

Author Info: (1) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. gs463@cam.ac.uk. Department of Physiology, Development and Neuroscience,

Author Info: (1) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. gs463@cam.ac.uk. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. gs463@cam.ac.uk. (2) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. RhyGaze, Basel, Switzerland. (3) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. (4) Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany. Max Planck Institute for the Physics of Complex Systems, Dresden, Germany. Center for Systems Biology, Dresden, Germany. (5) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. (6) Gurdon Institute, University of Cambridge, Cambridge, UK. (7) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. (8) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. (9) Wellcome Sanger Institute, Hinxton, UK. Cambridge Institute of Science, Altos Labs, Cambridge, UK. (10) Wellcome Sanger Institute, Hinxton, UK. (11) Wellcome Sanger Institute, Hinxton, UK. (12) Wellcome Sanger Institute, Hinxton, UK. (13) University Hospital Carl Gustav Carus Dresden, Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany. Institute of Pathology, University Hospital CGC Dresden, TU Dresden, Dresden, Germany. (14) Institute of Anatomy, Faculty of Medicine of TUD, University of Technology, Dresden, Germany. (15) Institute of Anatomy, Faculty of Medicine of TUD, University of Technology, Dresden, Germany. (16) Department of Gastroenterology, Guy's and St. Thomas' Hospital, London, UK. (17) Wellcome Sanger Institute, Hinxton, UK. Addenbrooke's Hospital, Cambridge University Hospital NHS Trust, Cambridge, UK. (18) Wellcome Sanger Institute, Hinxton, UK. Department of Oncology, University of Cambridge, Hutchison Research Centre, Cambridge Biomedical Campus, Cambridge, UK. (19) Max Planck Institute for the Physics of Complex Systems, Dresden, Germany. Arnold Sommerfeld Center for Theoretical Physics, Ludwigs-Maximilians-UniversitŠt Munchen, Munich, Germany. (20) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. Gurdon Institute, University of Cambridge, Cambridge, UK. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Science, University of Cambridge, Cambridge, UK. (21) Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK. mpa28@cam.ac.uk. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. mpa28@cam.ac.uk.

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