Journal Articles

Senescence-directed nanotherapy ameliorates fibrosis and overcomes immune exclusion in cancer

(1) Hinterleitner C (2) Barthet VJA (3) Goldberg HV (4) Vogt KC (5) Perea AM (6) Hillger LR (7) Ruiz S (8) McHugh D (9) Ho YJ (10) Chaves-Perez A (11) Skamagki M (12) Flowers S (13) Styers HC (14) Rekhtman N (15) Zhuang X (16) Dessotti Barretto G (17) Li X (18) Watson JT (19) Luan W (20) Simon J (21) Tammela T (22) Gardner R (23) Rudin CM (24) Romesser PB (25) Bott MJ (26) Filliol A (27) Heller DA (28) Lowe SW

Science

Hinterleitner, Barthet, and Goldberg et al. showed that senescent-like cells in fibrotic human and mouse tissues expressed P-selectin, and the team developed fucoidan-based senescence-modulating nanoparticles (SMNPs) to target these cells. SMNPs selectively depleted P-selectin+ senescent-like macrophages (Sen+MP) and reduced liver and lung fibrosis with limited toxicities. In fibrotic liver and lung tumor models, SMNPs remodeled stromal and myeloid barriers, increased DC and T cell infiltration, and improved response to ICB. P-selectin+ Sen+MP were enriched in immune-excluded human tumors and correlated with poor response to neoadjuvant ICB plus chemotherapy.

Contributed by Shishir Pant

(1) Hinterleitner C (2) Barthet VJA (3) Goldberg HV (4) Vogt KC (5) Perea AM (6) Hillger LR (7) Ruiz S (8) McHugh D (9) Ho YJ (10) Chaves-Perez A (11) Skamagki M (12) Flowers S (13) Styers HC (14) Rekhtman N (15) Zhuang X (16) Dessotti Barretto G (17) Li X (18) Watson JT (19) Luan W (20) Simon J (21) Tammela T (22) Gardner R (23) Rudin CM (24) Romesser PB (25) Bott MJ (26) Filliol A (27) Heller DA (28) Lowe SW

Science

Hinterleitner, Barthet, and Goldberg et al. showed that senescent-like cells in fibrotic human and mouse tissues expressed P-selectin, and the team developed fucoidan-based senescence-modulating nanoparticles (SMNPs) to target these cells. SMNPs selectively depleted P-selectin+ senescent-like macrophages (Sen+MP) and reduced liver and lung fibrosis with limited toxicities. In fibrotic liver and lung tumor models, SMNPs remodeled stromal and myeloid barriers, increased DC and T cell infiltration, and improved response to ICB. P-selectin+ Sen+MP were enriched in immune-excluded human tumors and correlated with poor response to neoadjuvant ICB plus chemotherapy.

Contributed by Shishir Pant

ABSTRACT: Fibrotic remodeling of tissues and tumors establishes immunosuppressive microenvironments that drive organ dysfunction and, in cancer, limit response to immunotherapy. Senescent-like cells are conserved drivers of fibrosis and therapeutic targets, yet their functional heterogeneity complicates therapeutic intervention. Here, we show that P-selectin is expressed by a subset of senescent-like cells in fibrotic tissues and tumors. Leveraging fucoidan-based nanoparticles that bind P-selectin, we developed senescence-modulating nanoparticles (SMNPs) to selectively target these disease-associated states. SMNPs exerted potent antifibrotic and immunomodulatory effects while improving the therapeutic index. Mechanistically, we identified a pathogenic, immunosuppressive macrophage population as a functional target in vivo. In fibrotic tumors, niche remodeling restored immune infiltration and sensitized tumors to immune checkpoint-based therapies. These findings establish SMNPs as a generalizable strategy to target pathogenic senescent cell subsets across fibrosis and cancer.

Author Info: (1) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (2) Cancer Biology and Genetics Program, Sloan Ketter

Author Info: (1) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (2) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (3) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA. (4) Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (5) Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Cancer Engineering Program, Gerstner Sloan Kettering School for Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (6) Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (7) Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (8) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (9) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (10) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (11) Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (12) Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (13) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Cancer Engineering Program, Gerstner Sloan Kettering School for Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (14) Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (15) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (16) Flow Cytometry Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (17) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (18) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (19) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (20) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (21) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (22) Flow Cytometry Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (23) Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (24) Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Early Drug Development Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (25) Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (26) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (27) Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (28) Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Citation: Science 2026 Sep 24 393:eaeg4791 Epub09/24/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42784692

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

(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

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

(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

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Distant lymph nodes compensate for resected tumor-draining lymph nodes during cancer immunotherapy

(1) Menzel L (2) Zhou H (3) Baish JW (4) O'Melia MJ (5) Darragh LB (6) Effiom DN (7) Specht E (8) Czapla J (9) Lei PJ (10) Rajotte JJ (11) Liu L (12) Nikmaneshi MR (13) Razavi MS (14) Vander Heiden MG (15) Ubellacker JM (16) Munn LL (17) Karam SD (18) Boland GM (19) Cohen S (20) Padera TP

Immunity

Menzel, Zhou et al. observed persisting responses to ICB in melanoma patients and orthotopic tumor mouse models upon complete regional tumor-draining lymph node (LN) dissection. Antigens moved through interstitial tissues, even crossing the midline, to an adjacent tissue region (lymphosome) that drains distant LNs. In these contralateral LNs, interstitial antigens were mainly presented by LN-resident cDC1s, rather than migratory DCs, and extended the ICB-mediated expansion of effector and memory T cells. In patients with head and neck cancer who were treated with neoadjuvant ICB, distant LNs became reactive in responders.

Contributed by Ute Burkhardt

(1) Menzel L (2) Zhou H (3) Baish JW (4) O'Melia MJ (5) Darragh LB (6) Effiom DN (7) Specht E (8) Czapla J (9) Lei PJ (10) Rajotte JJ (11) Liu L (12) Nikmaneshi MR (13) Razavi MS (14) Vander Heiden MG (15) Ubellacker JM (16) Munn LL (17) Karam SD (18) Boland GM (19) Cohen S (20) Padera TP

Immunity

Menzel, Zhou et al. observed persisting responses to ICB in melanoma patients and orthotopic tumor mouse models upon complete regional tumor-draining lymph node (LN) dissection. Antigens moved through interstitial tissues, even crossing the midline, to an adjacent tissue region (lymphosome) that drains distant LNs. In these contralateral LNs, interstitial antigens were mainly presented by LN-resident cDC1s, rather than migratory DCs, and extended the ICB-mediated expansion of effector and memory T cells. In patients with head and neck cancer who were treated with neoadjuvant ICB, distant LNs became reactive in responders.

Contributed by Ute Burkhardt

ABSTRACT: Surgical removal of tumor-draining lymph nodes (tdLNs) is commonly performed in cancer patients. Here, we investigated whether immune checkpoint blockade (ICB) responses persist after resection of tdLNs, important sites for the initiation and maintenance of anti-cancer immunity. Melanoma patients remained responsive to programmed death 1 (PD-1) blockade after regional LN dissection. Similarly, ICB efficacy persisted after tdLN resection in orthotopic murine melanoma and mammary carcinoma models. Following tdLN removal, interstitial fluid containing soluble antigen was diverted through interstitial spaces across lymphosome boundaries to distant LNs, wherein cancer-derived antigen was acquired and presented to T cells by LN-resident type I conventional dendritic cells. These responses persisted after primary tumor resection and supported ICB-induced systemic immunity. Locoregional delivery of ICB to compensatory LNs enhanced anti-cancer responses after tdLN resection. Consistently, ICB responses in head and neck cancer patients were associated with reactive LNs at distant sites. Thus, antigen rerouting enables distant LNs to compensate for resected tdLNs and sustain anti-cancer immunity, including responses enhanced by immunotherapy.

Author Info: (1) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (2) Edwin Steele Laboratories, Department of Radiation Oncol

Author Info: (1) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (2) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA; Koch Institute for Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. (3) Department of Biomedical Engineering, Bucknell University, Lewisburg, PA 17837, USA. (4) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (5) Department of Radiation Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. (6) Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA. (7) Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA. (8) Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA. (9) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (10) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (11) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (12) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (13) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (14) Koch Institute for Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (15) Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. (16) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. (17) Department of Radiation Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. (18) Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA. (19) Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA. (20) Edwin Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. Electronic address: tpadera@mgh.harvard.edu.

Citation: Immunity 2026 Oct 1 Epub10/01/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42822444

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Chemotherapy enhances cancer vaccine efficacy and expands stem-like TCF1+CD8+ T cells

(1) Noblecourt L (2) Wicki A (3) Jainarayanan A (4) Pereira-Almeida V (5) McAuliffe J (6) Steffke E (7) Panetti S (8) Fuchs H (9) Ramirez-Valdez RA (10) Chandrasekar V (11) Cai Y (12) Boekestijn S (13) Hill AVS (14) Gerard A (15) Welters MJP (16) Van der Burg SH (17) Pedroza-Pacheco I (18) Van den Eynde BJ (19) Leung CSK

Cancer cell

Noblecourt and Wicki et al. showed that carboplatin and paclitaxel (CarboTaxol) with viral vector vaccines expanded antigen-specific TCF1+CD8+ T cells during priming, slowed tumor growth, and improved survival in mouse models. Acting as an adjuvant, CarboTaxol induced early tumor- and vaccine-independent expansion of stem-like TCF1+CD8+ T cells that depended on TCF1/β-catenin activity. Adding PD-1 blockade to chemotherapy and cancer vaccines further improved tumor control and long-term survival. Expansion of TCF7+CD8+ T cells was also seen in ovarian and cervical cancer patients treated with CarboTaxol.

Contributed by Katherine Turner

(1) Noblecourt L (2) Wicki A (3) Jainarayanan A (4) Pereira-Almeida V (5) McAuliffe J (6) Steffke E (7) Panetti S (8) Fuchs H (9) Ramirez-Valdez RA (10) Chandrasekar V (11) Cai Y (12) Boekestijn S (13) Hill AVS (14) Gerard A (15) Welters MJP (16) Van der Burg SH (17) Pedroza-Pacheco I (18) Van den Eynde BJ (19) Leung CSK

Cancer cell

Noblecourt and Wicki et al. showed that carboplatin and paclitaxel (CarboTaxol) with viral vector vaccines expanded antigen-specific TCF1+CD8+ T cells during priming, slowed tumor growth, and improved survival in mouse models. Acting as an adjuvant, CarboTaxol induced early tumor- and vaccine-independent expansion of stem-like TCF1+CD8+ T cells that depended on TCF1/β-catenin activity. Adding PD-1 blockade to chemotherapy and cancer vaccines further improved tumor control and long-term survival. Expansion of TCF7+CD8+ T cells was also seen in ovarian and cervical cancer patients treated with CarboTaxol.

Contributed by Katherine Turner

ABSTRACT: Therapeutic cancer vaccines are increasingly tested in clinical settings alongside standard-of-care treatments that often include chemotherapy, yet whether chemotherapy synergizes with cancer vaccines remains unclear. Here, we tested heterologous prime-boost viral vector vaccines in combination with various chemotherapy regimens. Both carboplatin plus paclitaxel (CarboTaxol) and cyclophosphamide improve vaccine efficacy and enhance antigen-specific CD8(+) T cell responses. These chemotherapies act as immunological adjuvants independently of tumor presence. Mechanistically, CarboTaxol induces an early, antigen-independent expansion of stem-like T cell factor 1 (TCF1)(+)CD8(+) T cells, an effect also observed in patients with different cancer types. Genetic or pharmacological disruption of TCF1 impairs the immunological adjuvant effect of CarboTaxol. Adding programmed cell death 1 (PD-1) blockade to viral vector vaccines and CarboTaxol further improves tumor control and survival. Together, these findings identify a TCF1-dependent mechanism underlying the immune adjuvant effect of chemotherapy and provide a rationale for clinical evaluation of this triple combination therapy.

Author Info: (1) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (2) Ludwig Institute for Cancer Research, Nuffield Department of Medici

Author Info: (1) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (2) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (3) Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK. (4) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (5) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (6) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (7) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (8) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (9) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (10) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (11) Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (12) Department of Medical Oncology, Oncode Institute, Leiden University Medical Center, Albinusdreef, 2, 2333 ZA Leiden, the Netherlands. (13) Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (14) Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK. (15) Department of Medical Oncology, Oncode Institute, Leiden University Medical Center, Albinusdreef, 2, 2333 ZA Leiden, the Netherlands. (16) Department of Medical Oncology, Oncode Institute, Leiden University Medical Center, Albinusdreef, 2, 2333 ZA Leiden, the Netherlands. (17) Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (18) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, de Duve Institute, UCLouvain, Brussels, Belgium; WEL Research Institute, Brussels, Belgium. Electronic address: benoit.vandeneynde@ludwig.ox.ac.uk. (19) Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK. Electronic address: carol.leung@immonc.ox.ac.uk.

Citation: Cancer Cell 2026 Oct 2 Epub10/02/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42826713

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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

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Antibodies against HLA-E-VL9 enhance NK cell and CD8+ T cell cytotoxicity against tumor cells and HIV-infected CD4+ T cells

Spotlight 

(1) Hwang JK (2) Tuyishime M (3) Marston DJ (4) Yang H (5) Wrapp D (6) Li D (7) Brackenridge S (8) Frazier M (9) Levering NP (10) Scearce R (11) Rhodes B (12) Harris C (13) Quastel M (14) Kapingidza AB (15) Kliszczak AE (16) Gater J (17) Borrow P (18) Ferrari G (19) Gillespie GM (20) McMichael AJ (21) Haynes BF (22) Azoitei ML

Science translational medicine

Hwang et al. engineered high-affinity antibodies against HLA-E–VL9 using structure-based design and high-throughput library screening to block inhibitory NKG2A/CD94 interactions. HLA-E–VL9 mAbs enhanced direct NK cell killing and NK-mediated ADCC against HLA-E–VL9+ tumors in vitro and K562HLA-E–VL9 tumors in vivo. HIV-infected primary CD4+ T cells expressed HLA-E–VL9, and HLA-E–VL9 mAbs mediated NK cell ADCC that selectively eliminated activated or infected CD4+ T cells over resting CD4+ T cells. HLA-E–VL9 blockade also enhanced NKG2A+CD8+ T cell-mediated HIV-specific cytotoxicity.

Contributed by Shishir Pant

(1) Hwang JK (2) Tuyishime M (3) Marston DJ (4) Yang H (5) Wrapp D (6) Li D (7) Brackenridge S (8) Frazier M (9) Levering NP (10) Scearce R (11) Rhodes B (12) Harris C (13) Quastel M (14) Kapingidza AB (15) Kliszczak AE (16) Gater J (17) Borrow P (18) Ferrari G (19) Gillespie GM (20) McMichael AJ (21) Haynes BF (22) Azoitei ML

Science translational medicine

Hwang et al. engineered high-affinity antibodies against HLA-E–VL9 using structure-based design and high-throughput library screening to block inhibitory NKG2A/CD94 interactions. HLA-E–VL9 mAbs enhanced direct NK cell killing and NK-mediated ADCC against HLA-E–VL9+ tumors in vitro and K562HLA-E–VL9 tumors in vivo. HIV-infected primary CD4+ T cells expressed HLA-E–VL9, and HLA-E–VL9 mAbs mediated NK cell ADCC that selectively eliminated activated or infected CD4+ T cells over resting CD4+ T cells. HLA-E–VL9 blockade also enhanced NKG2A+CD8+ T cell-mediated HIV-specific cytotoxicity.

Contributed by Shishir Pant

ABSTRACT: A major natural killer (NK) cell and CD8(+) T cell checkpoint is mediated by the inhibitory receptor NKG2A/CD94 and its ligand, human leukocyte antigen E (HLA-E) complexed with nine-amino acid HLA-Ia leader sequence-derived peptides termed VL9 (HLA-E-VL9). Here, we used structure-based design and high-throughput library screening to generate high-affinity antibodies that block NKG2A/CD94 interactions. These antibodies enabled direct NK and CD8(+) T cell cytotoxicity and mediated NK cell antibody-dependent cellular cytotoxicity (ADCC). Anti-HLA-E-VL9 antibodies enhanced human NK cell line NK-92 killing of HLA-E-VL9(+) human tumors in mice, demonstrating checkpoint inhibition activity in vivo. Moreover, HLA-E-VL9 was found to be expressed on primary human CD4(+) T cells infected with HIV in vitro, and its engagement by HLA-E-VL9 antibodies drove elimination of infected cells by NK cell-mediated ADCC. HLA-E-VL9 antibodies also enhanced the killing of HIV-infected cells by NKG2A/CD94(+) CD8(+) T cells targeting an HIV Rev-derived epitope that complexes with HLA-E. Therefore, anti-HLA-E-VL9 antibodies represent a candidate therapeutic approach to eliminating pathogenic target cells by enhancing both NK cell and CD8(+) T cell function and by promoting ADCC.

Author Info: (1) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (2) Department o

Author Info: (1) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (2) Department of Surgery, Duke University School of Medicine, Durham, NC, USA. (3) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA. (4) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (5) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (6) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (7) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (8) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA. (9) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (10) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (11) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA. (12) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA. (13) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (14) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. (15) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (16) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. (17) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (18) Department of Surgery, Duke University School of Medicine, Durham, NC, USA. (19) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (20) Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. (21) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. Department of Integrative Immunobiology, Duke University School of Medicine, Durham, NC, USA. (22) Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, USA. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA.

Citation: Sci Transl Med 2026 Sep 16 18:eaea3879 Epub09/16/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42748220

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:

Inducible IL-12 or IL-18 secreting CAR T cells targeting the glyco-antigen CD176 exhibit potent activity against non-small cell lung cancer in preclinical models Spotlight 

(1) Malinconico C (2) Weber J (3) Polzien AJ (4) Bonifacius A (5) Umland M (6) Deveuve Q (7) Bargmann T (8) Zimmermann K (9) Gellert J (10) Neubert L (11) DŸbel S (12) Schambach A (13) Braun A (14) Blasczyk R (15) Abken H (16) Hudecek M (17) Dragon AC (18) Eiz-Vesper B

Cancer immunology research

Malinconico et al. engineered T cells with both a CAR targeting CD176 (expressed in NSCLC and other tumors), and an inducible cassette encoding either IL-18 or IL-12 that would be expressed following CAR activation of NF-κB. The resulting CD176-iIL18 or CD176-iIL12 TRUCKs showed antigen-dependent secretion of IL-18/IL-12, increased effector molecules, and heightened THP-1 monocyte chemoattraction. They also showed increased cytotoxicity and antitumor activity in vitro, against patient explants, and in vivo, and performed best when both CD4+ and CD8+ T cells were included. TRUCKs producing IL-12 outperformed those producing IL-18, but with more toxicity.

Contributed by Lauren Hitchings

(1) Malinconico C (2) Weber J (3) Polzien AJ (4) Bonifacius A (5) Umland M (6) Deveuve Q (7) Bargmann T (8) Zimmermann K (9) Gellert J (10) Neubert L (11) DŸbel S (12) Schambach A (13) Braun A (14) Blasczyk R (15) Abken H (16) Hudecek M (17) Dragon AC (18) Eiz-Vesper B

Cancer immunology research

Malinconico et al. engineered T cells with both a CAR targeting CD176 (expressed in NSCLC and other tumors), and an inducible cassette encoding either IL-18 or IL-12 that would be expressed following CAR activation of NF-κB. The resulting CD176-iIL18 or CD176-iIL12 TRUCKs showed antigen-dependent secretion of IL-18/IL-12, increased effector molecules, and heightened THP-1 monocyte chemoattraction. They also showed increased cytotoxicity and antitumor activity in vitro, against patient explants, and in vivo, and performed best when both CD4+ and CD8+ T cells were included. TRUCKs producing IL-12 outperformed those producing IL-18, but with more toxicity.

Contributed by Lauren Hitchings

ABSTRACT: To date, non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths worldwide, underscoring the urgent need for new treatment options. The oncofetal carbohydrate CD176 is masked on healthy tissues but is present on a variety of cancer entities and is associated with cancer invasiveness and metastasis. In this study, we employed chimeric antigen receptor T cells (CAR-Ts) directed against CD176 for treatment of NSCLC. CD176-CAR-Ts were optimized with an additional inducible cassette encoding IL-18 (CD176-iIL18-TRUCKs) or IL-12 (CD176-iIL12-TRUCKs) to augment antitumor reactivity through autocrine and paracrine signaling. CD176-iIL18- and CD176-iIL12-TRUCKs eradicate NSCLC cells in a 3D tumor spheroid model and tissue slices derived from lung adenocarcinoma patients more potently than CD176-CAR-Ts. Administration of TRUCKs in a lung carcinoma xenograft mouse model results in partial or complete tumor eradication in all mice treated with CD176-iIL12-TRUCKs and in 50% of mice treated with CD176-iIL18-TRUCKs. This study highlights the potential of CD176 as a CAR-T-cell target and suggest CD176-CAR-Ts armored with IL-18 or IL-12 as promising new therapeutic approach for the treatment of NSCLC and several other CD176-positive cancer entities.

Author Info: (1) Italian Institute of Technology Napoli Italy. ROR: https://ror.org/042t93s57 (2) University Hospital WŸrzburg WŸrzburg Germany. (3) Hannover Medical School Hannover Germany. (4

Author Info: (1) Italian Institute of Technology Napoli Italy. ROR: https://ror.org/042t93s57 (2) University Hospital WŸrzburg WŸrzburg Germany. (3) Hannover Medical School Hannover Germany. (4) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98 (5) Hannover Medical School Hannover Germany. (6) UniversitŠtsklinikum WŸrzburg WŸrzburg Germany. (7) Fraunhofer Institute for Toxicology and Experimental Medicine Hannover, Lower Saxony Germany. ROR: https://ror.org/02byjcr11 (8) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98 (9) Glycotope GmbH Berlin Germany. (10) Medizinische Hochschule Hannover Hannover, Low Saxony Germany. ROR: https://ror.org/00f2yqf98 (11) Technische UniversitŠt Braunschweig Braunschweig Germany. ROR: https://ror.org/010nsgg66 (12) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98 (13) Fraunhofer Institute for Toxicology and Experimental Medicine Hannover, Lower Saxony Germany. ROR: https://ror.org/02byjcr11 (14) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98 (15) Leibniz Institute for Immunotherapy and Univ Regensberg Regensburg Germany. (16) UniversitŠtsklinikum WŸrzburg WŸrzburg Germany. ROR: https://ror.org/03pvr2g57 (17) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98 (18) Medizinische Hochschule Hannover Hannover Germany. ROR: https://ror.org/00f2yqf98

Citation: Cancer Immunol Res 2026 Sep 22 Epub09/22/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42770707

Tags:
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