Journal Articles

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

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

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.

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

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.

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.

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

Spotlight 

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

Contributed by Paula Hochman

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

Contributed by Paula Hochman

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

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

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

Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

Spotlight 

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

Contributed by Katherine Turner

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

Contributed by Katherine Turner

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

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

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

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

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

Contributed by Shishir Pant

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

Contributed by Shishir Pant

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

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

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

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

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

Contributed by Lauren Hitchings

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

Contributed by Lauren Hitchings

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

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

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

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

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

Contributed by Shishir Pant

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

Contributed by Shishir Pant

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

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

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

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

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

Contributed by Paula Hochman

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

Contributed by Paula Hochman

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

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

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

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

Spotlight 

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

Contributed by Ed Fritsch

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

Contributed by Ed Fritsch

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

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

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

Tumor immune microenvironment remodeling predicts response to checkpoint inhibitor therapy Spotlight 

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

Contributed by Shishir Pant

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

Contributed by Shishir Pant

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

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

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

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