Journal Articles

In situ reprogramming of glioma stem cells into dendritic cell-like cells for glioblastoma immunotherapy

Spotlight 

Chunwei Tang (1,4), Weiyi Sun (1,4), Zhenmei Yang (1,4), Xiaotian Zhao (1), Jian Gao (1), Kun Han (1), Zichao Yao (1), Xianghui Dong (1), Miaoyan Liu (1), Jing Zhang (1), Zhaoyu Chen (2), Xiao Yue (1), Zhipeng Fu (1), Yan Wang (1), Maosen Han (1), Ying Liu (1), Jinxin Gao (1), Yulin Zhang (3), Xinyi Jiang (1), Chen Chen (1), Xin Wen (1), Kun Zhao (1) & Jingyi Han (1).

Nature communications

Tang, Sun, Yang, et al. reprogrammed glioma stem cells (GSCs) into cDC1-like cells in situ by administering a hydrogel to the post-op tumor cavity for slow and local delivery of CD133-targeting LNPs carrying plasmids encoding PIB (PU.1, IRF8, BATF3) and costimulatory TNFSF9. In vivo, GSCs were preferentially targeted and acquired a cDC1-like phenotype with increased antigen presentation, costimulatory signaling, and proinflammatory cytokine secretion, supporting enhanced antigen-specific effector and memory T cell responses that cleared tumor cells, improved survival and neurological outcomes, and protected against relapse and rechallenge.

Contributed by Lauren Hitchings

Chunwei Tang (1,4), Weiyi Sun (1,4), Zhenmei Yang (1,4), Xiaotian Zhao (1), Jian Gao (1), Kun Han (1), Zichao Yao (1), Xianghui Dong (1), Miaoyan Liu (1), Jing Zhang (1), Zhaoyu Chen (2), Xiao Yue (1), Zhipeng Fu (1), Yan Wang (1), Maosen Han (1), Ying Liu (1), Jinxin Gao (1), Yulin Zhang (3), Xinyi Jiang (1), Chen Chen (1), Xin Wen (1), Kun Zhao (1) & Jingyi Han (1).

Nature communications

Tang, Sun, Yang, et al. reprogrammed glioma stem cells (GSCs) into cDC1-like cells in situ by administering a hydrogel to the post-op tumor cavity for slow and local delivery of CD133-targeting LNPs carrying plasmids encoding PIB (PU.1, IRF8, BATF3) and costimulatory TNFSF9. In vivo, GSCs were preferentially targeted and acquired a cDC1-like phenotype with increased antigen presentation, costimulatory signaling, and proinflammatory cytokine secretion, supporting enhanced antigen-specific effector and memory T cell responses that cleared tumor cells, improved survival and neurological outcomes, and protected against relapse and rechallenge.

Contributed by Lauren Hitchings

ABSTRACT: Immunotherapy has revolutionized the treatment landscape for many cancers, but fails to improve the outcomes of glioblastoma multiforme (GBM), in which low tumor antigen presentation restricts the recognition and eradication of cancerous cells by immunocytes. We here report an approach to in situ reprogramming hypoimmunogenic glioblastoma stem cells (GSCs) into type 1 conventional dendritic cell (cDC1)-like cells for presenting endogenous tumor antigens and priming sustained tumoricidal T-cell responses. Specifically, our cavity-injectable nucleobase-derived lipid nanoparticle (LNP)-hydrogel superstructure efficiently delivers transcription factors (PU.1, IRF8, BATF3) and the costimulatory molecule (TNFSF9) into GSCs to induce cDC1-like reprogramming. These cDC1-like cells potentiate GBM immunogenicity, elicit robust adaptive immunity and long-term antitumor memory, thereby inhibiting tumor growth and preventing postoperative GBM relapse in mice. Collectively, this work establishes in situ tumor cell reprogramming into licensed cDC1-like cells as a therapeutic paradigm for converting immunologically “cold” tumors into a self-vaccinating immune niche with broad translational potential for hypoimmunogenic malignancy treatment.

Author Info: (1) Department of Thoracic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong Key Laboratory of Targeted Drug Delivery and Advanced Pharmaceutics, NMPA Key Laboratory fo

Author Info: (1) Department of Thoracic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong Key Laboratory of Targeted Drug Delivery and Advanced Pharmaceutics, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products and Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.(2) Shandong University of Traditional Chinese Medicine, Jinan, China. (3) Department of Neurosurgery, Qilu Hospital and Institute of Brain and Brain-Inspired Science, Cheeloo College of Medicine, Shandong University, Jinan, China. (4)These authors contributed equally: Chunwei Tang, Weiyi Sun, Zhenmei Yang. e-mail: xinyijiang@sdu.edu.cn; chenchen27@sdu.edu.cn; wxzbc@mail.sdu.edu.cn; kzhao2013@sdu.edu.cn; jyhantw@email.sdu.edu.cn

Citation: nat com 2026

Tags:

    A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions Spotlight 

    (1) Cao K (2) Li R (3) Stra_ar M (4) Brown EM (5) Nguyen PNU (6) Pust MM (7) Park J (8) Graham DB (9) Ashenberg O (10) Uhler C (11) Xavier RJ

    Nature communications | Free PMC Article

    Cao et al. developed StriMap – a prediction framework for modeling peptide–HLA presentation and TCR recognition as a coupled process based on physicochemical, sequence-context, and structure-informed representations that capture relevant constraints. StriMap was successfully used to screen bacterial peptides and identity candidates to activate TCRs associated with ankylosing spondylitis (AS), and a top candidate in AS was also confirmed in IBD, linking the two autoimmune diseases. In cancer, StriMap demonstrated efficacy for both TCR selection in adoptive cell therapy, and prioritizing potential neoepitopes for personalized cancer vaccines.

    Contributed by Lauren Hitchings

    (1) Cao K (2) Li R (3) Stra_ar M (4) Brown EM (5) Nguyen PNU (6) Pust MM (7) Park J (8) Graham DB (9) Ashenberg O (10) Uhler C (11) Xavier RJ

    Nature communications | Free PMC Article

    Cao et al. developed StriMap – a prediction framework for modeling peptide–HLA presentation and TCR recognition as a coupled process based on physicochemical, sequence-context, and structure-informed representations that capture relevant constraints. StriMap was successfully used to screen bacterial peptides and identity candidates to activate TCRs associated with ankylosing spondylitis (AS), and a top candidate in AS was also confirmed in IBD, linking the two autoimmune diseases. In cancer, StriMap demonstrated efficacy for both TCR selection in adoptive cell therapy, and prioritizing potential neoepitopes for personalized cancer vaccines.

    Contributed by Lauren Hitchings

    ABSTRACT: The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite substantial advances in peptide-HLA presentation prediction, accurate modeling of coupled TCR-peptide-HLA recognition remains underdeveloped, limiting applications such as TCR and neoepitope prioritization in cancer and antigen identification in autoimmunity. Here we present StriMap, a unified framework for predicting TCR-peptide-HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces. StriMap achieves state-of-the-art performance with improved generalizability and enables applications in both cancer and autoimmunity. As a case study in ankylosing spondylitis (AS), we screened 13 million peptides derived from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an AS-associated TCR. Notably, a top validated peptide was enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers between AS and IBD. Overall, StriMap provides a generalizable framework for rational immunotherapy design and for dissecting antigenic drivers of autoimmunity.

    Author Info: (1) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (2) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Department of Molecular Biology, Massach

    Author Info: (1) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (2) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. (3) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (4) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (5) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (6) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. (7) Broad Institute of MIT and Harvard, Cambridge, MA, United States. (8) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. (9) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, United States. (10) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Massachusetts Institute of Technology, Cambridge, MA, United States. (11) Broad Institute of MIT and Harvard, Cambridge, MA, United States. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, United States. Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA, United States.

    Citation: Nat com 09/25/26

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/41959488

    Tags:

    Peripheral Blood as the Sole Source for Cancer Neoantigen and Reactive T-Cell Discovery Spotlight 

    (1) Garcia-Garijo A (2) Creus-Gonz‡lez I (3) Levy PL (4) Moreno-C‡rdenas AB (5) Prieto-Fern‡ndez E (6) Lozano-Rabella M (7) Fajardo CA (8) Pujol-Borrell R (9) Mar’n-Bayo A (10) Kusnick J (11) Gonzalez J (12) Brujas C (13) Gartner JJ (14) Yuste-Estevanez A (15) Calahorro AM (16) Louhamane A (17) Fernandez J (18) Sanz-Pamplona R (19) Watkins TBK (20) Galvao V (21) Munoz-Couselo E (22) Ros J (23) ƒlez E (24) Oliveira M (25) Saura Manich C (26) Brana I (27) Oaknin A (28) Alemany R (29) Tabernero J (30) Perez-Lopez R (31) Turcotte S (32) Frankell AM (33) Garralda E (34) Toledo RA (35) Gros A

    Cancer discovery

    To expand the accessibility of neoantigen-based immunotherapy, Garcia-Garijo et al. integrated circulating tumor DNA (ctDNA) whole-exome sequencing (WES) to identify non-synonymous mutations (NSMs) with the isolation of neoantigen-specific T cells from circulating CD8+PD1hiCD38+ and CD4+PD1hi populations. In 8 patients with metastatic solid cancers, this strategy identified most lesion-derived NSMs (and additional neoantigens) enriched for clonal variants. In a cohort of 69 patients with metastatic solid cancers, neoantigen discovery based only on ctDNA seemed feasible in ~50% of patients (~85% of patients with colorectal cancer).

    Contributed by Ute Burkhardt

    (1) Garcia-Garijo A (2) Creus-Gonz‡lez I (3) Levy PL (4) Moreno-C‡rdenas AB (5) Prieto-Fern‡ndez E (6) Lozano-Rabella M (7) Fajardo CA (8) Pujol-Borrell R (9) Mar’n-Bayo A (10) Kusnick J (11) Gonzalez J (12) Brujas C (13) Gartner JJ (14) Yuste-Estevanez A (15) Calahorro AM (16) Louhamane A (17) Fernandez J (18) Sanz-Pamplona R (19) Watkins TBK (20) Galvao V (21) Munoz-Couselo E (22) Ros J (23) ƒlez E (24) Oliveira M (25) Saura Manich C (26) Brana I (27) Oaknin A (28) Alemany R (29) Tabernero J (30) Perez-Lopez R (31) Turcotte S (32) Frankell AM (33) Garralda E (34) Toledo RA (35) Gros A

    Cancer discovery

    To expand the accessibility of neoantigen-based immunotherapy, Garcia-Garijo et al. integrated circulating tumor DNA (ctDNA) whole-exome sequencing (WES) to identify non-synonymous mutations (NSMs) with the isolation of neoantigen-specific T cells from circulating CD8+PD1hiCD38+ and CD4+PD1hi populations. In 8 patients with metastatic solid cancers, this strategy identified most lesion-derived NSMs (and additional neoantigens) enriched for clonal variants. In a cohort of 69 patients with metastatic solid cancers, neoantigen discovery based only on ctDNA seemed feasible in ~50% of patients (~85% of patients with colorectal cancer).

    Contributed by Ute Burkhardt

    ABSTRACT: Neoantigen-targeted T-cell therapies require invasive tumor biopsies to identify neoantigens and reactive T cells, limiting their applicability and potentially underestimating tumor heterogeneity. We propose a novel strategy relying solely on peripheral blood to identify non-synonymous mutations via cell-free DNA whole-exome sequencing and isolate neoantigen-specific CD4+ and CD8+ T cells based on PD-1 and CD39 co-expression. Our method identified most neoantigens and increased the number of T-cell reactivities detected by standard biopsy-based techniques in an initial cohort of eight metastatic cancer patients. Critically, our approach uncovered additional neoantigens missed by tumor biopsy analysis, highlighting the potential for broader neoantigen targeting using liquid biopsies. Analysis of a cohort of 69 metastatic cancer patients revealed that approximately half could be eligible for our blood-only approach. This proof-of-concept study demonstrates the potential of using blood as a surrogate or complement for tumor biopsies to develop neoantigen-reactive T-cell therapies against a broader spectrum of neoantigens.

    Author Info: (1) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (2) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://

    Author Info: (1) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (2) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (3) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (4) Vall d'Hebron Institute of Oncology Spain. ROR: https://ror.org/054xx3904 (5) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (6) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (7) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (8) Universitat Aut˜noma de Barcelona Barcelona, Barcelona Spain. ROR: https://ror.org/052g8jq94 (9) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (10) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (11) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (12) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (13) National Institutes of Health United States. ROR: https://ror.org/01cwqze88 (14) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (15) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (16) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (17) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (18) Instituto de Investigaci—n Sanitaria Arag—n Zaragoza Spain. ROR: https://ror.org/03njn4610 (19) Stanford University Stanford, CA United States. ROR: https://ror.org/00f54p054 (20) Vall d'Hebron Institute of Oncology Barcelona Spain. ROR: https://ror.org/054xx3904 (21) Vall d'Hebron Institute of Oncology barcelona, barcelona, spain Spain. ROR: https://ror.org/054xx3904 (22) Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain. (23) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (24) Vall d'Hebron Institute of Oncology Barcelona Spain. ROR: https://ror.org/054xx3904 (25) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (26) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (27) Vall d'Hebron Institute of Oncology Barcelona Spain. ROR: https://ror.org/054xx3904 (28) Institut d'Investigaci— BiomŽdica de Bellvitge L'Hospitalet de Llobregat Spain. ROR: https://ror.org/0008xqs48 (29) Vall d'Hebron Institute of Oncology Barcelona Spain. ROR: https://ror.org/054xx3904 (30) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (31) Centre Hospitalier de l'UniversitŽ de MontrŽal Montreal Canada. ROR: https://ror.org/0410a8y51 (32) The Francis Crick Institute London United Kingdom. ROR: https://ror.org/04tnbqb63 (33) Vall d'Hebron Institute of Oncology Barcelona Spain. ROR: https://ror.org/054xx3904 (34) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904 (35) Vall d'Hebron Institute of Oncology Barcelona, Barcelona Spain. ROR: https://ror.org/054xx3904

    Citation: Cancer Discov 2026 Sep 29 Epub09/29/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42806482

    Tags:

    Computational design of optimized and preferentially paired human TCR constant regions for improved T cell function Spotlight 

    (1) Radman M (2) Shamul-Raybi A (3) Parker J (4) Fleishman SJ (5) Cohen CJ

    Science advances | Free PMC Article

    Using a computational strategy, Radman et al. mutated the constant and transmembrane regions of the MART-1 DMF4 TCR α/β chains to reduce mispairing and improve expression. TCR variants were found that increased expression and TNFα production against MART-1+ cells compared to the WT TCR, and the modified α/β chains expressed better together than with their WT counterparts. Modified TCR-T cells generated by retroviral vector had superior cytokine production to WT TCR-T (in bulk T cells and CD4+ or CD8+ T cells), as well as cytotoxicity and in vivo efficacy. This same strategy was successfully applied to other antitumor and antiviral TCRs.

    Contributed by Alex Najibi

    (1) Radman M (2) Shamul-Raybi A (3) Parker J (4) Fleishman SJ (5) Cohen CJ

    Science advances | Free PMC Article

    Using a computational strategy, Radman et al. mutated the constant and transmembrane regions of the MART-1 DMF4 TCR α/β chains to reduce mispairing and improve expression. TCR variants were found that increased expression and TNFα production against MART-1+ cells compared to the WT TCR, and the modified α/β chains expressed better together than with their WT counterparts. Modified TCR-T cells generated by retroviral vector had superior cytokine production to WT TCR-T (in bulk T cells and CD4+ or CD8+ T cells), as well as cytotoxicity and in vivo efficacy. This same strategy was successfully applied to other antitumor and antiviral TCRs.

    Contributed by Alex Najibi

    ABSTRACT: T cell receptor (TCR) gene transfer is a promising approach for cancer immunotherapy, but its efficacy is limited by mispairing of TCR chains, which reduces surface expression and may generate off-target specificities. We applied the PROSS and FuncLib algorithms to engineer human TCR constant regions for improved stability and preferential pairing. After several screening rounds, we identified a variant, termed structurally enhanced TCR (SET), that exhibited markedly enhanced surface expression, functional avidity, and reduced mispairing. SET-expressing T cells secreted higher cytokine levels, displayed increased activation, and mediated superior cytotoxicity. Notably, SET demonstrated function in CD4(+) T cells and mediated potent tumor control in xenograft models, significantly delaying tumor growth and improving survival. SET's benefits were reproducible across six different TCRs, supporting its broad applicability. These findings highlight the potential of rational design to improve the potency of TCR-based therapies.

    Author Info: (1) The Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel. (2) The Laboratory of Tumor

    Author Info: (1) The Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel. (2) The Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel. (3) Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel. (4) Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel. (5) The Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.

    Citation: Sci Adv 2026 Sep 25 12:eaeb9189 Epub09/23/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42777048

    Tags:

    Functional evaluation of TCR-pMHC pairs at scale allows in silico TCR reactivity prediction Featured  

    (1) Messemaker M (2) Kwee BPY (3) Moravec _ (4) de Paauw S (5) Urbanus J (6) çlvarez-Salmoral D (7) Geerligs J (8) Xie R (9) Voogd R (10) Morris B (11) Guislain A (12) Winkler Y (13) Richterich CA (14) Mussmann M (15) Bakker M (16) Steinmetz M (17) Iras M (18) Marcus E (19) Teuwen J (20) Perrakis A (21) Beijersbergen RL (22) Scheper W (23) Schumacher TN

    Immunity

    Messemaker and Kwee et al. developed a TCR rapid assembly platform (T-RAP) and used it to generate a dataset of functionally validated TCR-pMHC pairs, which enabled the assembly of diverse and standardized TCR libraries and allowed for structure-based prediction of TCR-pMHC reactivity. Pooled screening of these TCR libraries could then be used to evaluate the quality of existing reactivity prediction models, including tcrdist3 and AlphaFold3, showed strong capacities for distinguishing reactivity and could be used both to pair TCRs with cognate pMHC epitopes and to shortlist TCRs with reactivity to patient-specific neoantigens, without any task-specific training.

    (1) Messemaker M (2) Kwee BPY (3) Moravec _ (4) de Paauw S (5) Urbanus J (6) çlvarez-Salmoral D (7) Geerligs J (8) Xie R (9) Voogd R (10) Morris B (11) Guislain A (12) Winkler Y (13) Richterich CA (14) Mussmann M (15) Bakker M (16) Steinmetz M (17) Iras M (18) Marcus E (19) Teuwen J (20) Perrakis A (21) Beijersbergen RL (22) Scheper W (23) Schumacher TN

    Immunity

    Messemaker and Kwee et al. developed a TCR rapid assembly platform (T-RAP) and used it to generate a dataset of functionally validated TCR-pMHC pairs, which enabled the assembly of diverse and standardized TCR libraries and allowed for structure-based prediction of TCR-pMHC reactivity. Pooled screening of these TCR libraries could then be used to evaluate the quality of existing reactivity prediction models, including tcrdist3 and AlphaFold3, showed strong capacities for distinguishing reactivity and could be used both to pair TCRs with cognate pMHC epitopes and to shortlist TCRs with reactivity to patient-specific neoantigens, without any task-specific training.

    ABSTRACT: Accurate prediction of T cell receptor (TCR) reactivity is a long-standing goal in immunology. Here, we asked whether functional validation of TCR-peptide-major histocompatibility complex (MHC) (TCR-pMHC) pairs at scale alters performance estimates of TCR-pMHC reactivity prediction models. We developed a TCR rapid assembly platform (T-RAP) that allowed the generation of large and uniform TCR libraries. T-RAP enabled evaluation of TCR signaling or MHC multimer binding of thousands of previously reported TCR-pMHC pairs under standardized conditions. In this setting of systematic standardized evaluation, only _50% of these TCRs showed the previously reported TCR reactivity. Notably, AlphaFold3 structural predictions showed good performance in identifying reactive TCR-pMHC pairs within the set of TCRs that experimentally validated without any task-specific training. AlphaFold3 structural predictions could likewise be used to shortlist TCRs reactive to patient-specific cancer neoantigens. In silico prediction of TCR-pMHC reactivity thus has greater feasibility than previously assumed, with implications for basic research and clinical application.

    Author Info: (1) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (2) Division of Molecular

    Author Info: (1) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (2) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (3) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (4) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (5) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (6) Oncode Institute, Utrecht, the Netherlands; Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (7) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands; The NKI Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (8) Oncode Institute, Utrecht, the Netherlands; Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (9) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (10) The NKI Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (11) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (12) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (13) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (14) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (15) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (16) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (17) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands. (18) AI for Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (19) AI for Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (20) Oncode Institute, Utrecht, the Netherlands; Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (21) The NKI Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (22) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (23) Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: t.schumacher@nki.nl.

    Citation: Immunity 2026 Oct 1 Epub10/01/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42822443

    Tags:

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

    Spotlight 

    (1) Xu W (2) Guo J (3) Cao X (4) Li L (5) Xiao P (6) Zhang X (7) Jin Q (8) Zhang F (9) Hou B (10) Li M (11) Zhou X

    Nature immunology

    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

    (1) Xu W (2) Guo J (3) Cao X (4) Li L (5) Xiao P (6) Zhang X (7) Jin Q (8) Zhang F (9) Hou B (10) Li M (11) Zhou X

    Nature immunology

    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.

    Citation: Nat Immunol 2026 Sep 11 Epub09/11/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42728401

    Tags:

    Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

    Spotlight 

    (1) St Paul M (2) Hendrikse LD (3) Ying F (4) Snow BE (5) Luo P (6) Helke S (7) Smith LK (8) Hilal A (9) Abelman DD (10) Ramamurthy N (11) Nault H (12) Wei EN (13) Gold MJ (14) Tobin C (15) Lien SC (16) Liu Y (17) Zhou W (18) Zhang X (19) Nguyen D (20) Agbede O (21) Pedersen S (22) Eagles J (23) Saunders ME (24) Berger T (25) Wakeham A (26) Scott DS (27) Ly D (28) de Campos CB (29) Liang G (30) Zheng C (31) Wilson WV (32) Masih-Khan E (33) White D (34) McCurdy A (35) Louzada ML (36) Kotb R (37) Chu MP (38) Parkin S (39) Reece D (40) Gul E (41) Tiedemann R (42) Pugh TJ (43) Hirano N (44) Ohashi PS (45) Stewart AK (46) Trudel S (47) Mak TW

    Nature

    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

    (1) St Paul M (2) Hendrikse LD (3) Ying F (4) Snow BE (5) Luo P (6) Helke S (7) Smith LK (8) Hilal A (9) Abelman DD (10) Ramamurthy N (11) Nault H (12) Wei EN (13) Gold MJ (14) Tobin C (15) Lien SC (16) Liu Y (17) Zhou W (18) Zhang X (19) Nguyen D (20) Agbede O (21) Pedersen S (22) Eagles J (23) Saunders ME (24) Berger T (25) Wakeham A (26) Scott DS (27) Ly D (28) de Campos CB (29) Liang G (30) Zheng C (31) Wilson WV (32) Masih-Khan E (33) White D (34) McCurdy A (35) Louzada ML (36) Kotb R (37) Chu MP (38) Parkin S (39) Reece D (40) Gul E (41) Tiedemann R (42) Pugh TJ (43) Hirano N (44) Ohashi PS (45) Stewart AK (46) Trudel S (47) Mak TW

    Nature

    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.

    Citation: Nature 2026 Sep 16 Epub09/16/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42749802

    Tags:

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

    (1) Duzagac F (2) Deng N (3) Wang J (4) Nagaraju S (5) Steinberger P (6) Reuben A (7) Sinha KM (8) Vilar E

    Cell reports. Medicine

    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

    (1) Duzagac F (2) Deng N (3) Wang J (4) Nagaraju S (5) Steinberger P (6) Reuben A (7) Sinha KM (8) Vilar E

    Cell reports. Medicine

    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.

    Citation: Cell Rep Med 2026 Sep 18 103057 Epub09/18/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42759506

    Tags:

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

    (1) Haratani K (2) Reinhold B (3) Duke-Cohan JS (4) Fahey CG (5) Tan K (6) Mallis RJ (7) Gusev A (8) Kehl KL (9) Luo J (10) Karmazyn A (11) Holliday EL (12) Masi DJ (13) Zienkiewicz KJ (14) Hennessey CJ (15) Blasco RB (16) Thai TC (17) Gibbons GM (18) Kivlehan S (19) Lizotte P (20) Paweletz CP (21) Aguirre AJ (22) Ligon KL (23) Chiarle R (24) Lang MJ (25) Barbie DA (26) Reinherz EL

    Immunity

    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

    (1) Haratani K (2) Reinhold B (3) Duke-Cohan JS (4) Fahey CG (5) Tan K (6) Mallis RJ (7) Gusev A (8) Kehl KL (9) Luo J (10) Karmazyn A (11) Holliday EL (12) Masi DJ (13) Zienkiewicz KJ (14) Hennessey CJ (15) Blasco RB (16) Thai TC (17) Gibbons GM (18) Kivlehan S (19) Lizotte P (20) Paweletz CP (21) Aguirre AJ (22) Ligon KL (23) Chiarle R (24) Lang MJ (25) Barbie DA (26) Reinherz EL

    Immunity

    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.

    Citation: Immunity 2026 Sep 17 Epub09/17/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42753739

    Tags:

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

    (1) Falcomatà C (2) Schaefer MM (3) Singh B (4) Chhamalwan D (5) Tepper A (6) Nielsen SR (7) Potak HT (8) Dhainaut M (9) Mollaoglu G (10) Park MD (11) Merad M (12) Baccarini A (13) Brown BD

    Nature

    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

    (1) Falcomatà C (2) Schaefer MM (3) Singh B (4) Chhamalwan D (5) Tepper A (6) Nielsen SR (7) Potak HT (8) Dhainaut M (9) Mollaoglu G (10) Park MD (11) Merad M (12) Baccarini A (13) Brown BD

    Nature

    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.

    Citation: Nature 2026 Sep 9 Epub09/09/2026

    Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42717081

    Tags:
    Close Modal

    Subscribe to our mailing list

    GDPR Marketing Permissions

    The Fritsch Foundation/ACIR.org will use the information you provide on this form to be in touch with you and to send you a weekly digest on cance immunotherapy. Click below to confirm that you want to receive the weekly digest by:

    You can change your mind at any time by clicking the unsubscribe link in the footer of any email you receive from us, or by contacting us at contact@acir.org. We will treat your information with respect and will never share it with any third-parties. By clicking below, you agree that we may process your information in accordance with these terms.

    We use Mailchimp as our marketing platform. By clicking below to subscribe, you acknowledge that your information will be transferred to Mailchimp for processing. Learn more about Mailchimp's privacy practices here.

    Close Modal

    Small change for you. Big change for us!

    This Thanksgiving season, show your support for cancer research by donating your change.

    In less than a minute, link your credit card with our partner RoundUp App.

    Every purchase you make with that card will be rounded up and the change will be donated to ACIR.

    All transactions are securely made through Stripe.