Journal Articles

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.

PLA2G2D in tumour-draining lymph nodes regulates anti-tumour immunity Featured  

Van Krimpen and Huang et al. performed spatial proteogenomics studies on tdLNs from patients with melanoma. In addition to identifying a spatial neighborhood in the tdLN paracortex linked to poor prognosis, the researchers also identified a population of lymph node macrophages secreting PLA2G2D, which acted as an immune checkpoint that limited CD8+ T cell-mediated antitumor immunity. Blocking PLA2G2D reduced immunosuppression and enhanced antitumor immunity, resulting in reduced tumor growth, which could be further reduced in combination with anti-PD-1. PLA2G2D was also associated with poor prognosis in patient data.

Van Krimpen and Huang et al. performed spatial proteogenomics studies on tdLNs from patients with melanoma. In addition to identifying a spatial neighborhood in the tdLN paracortex linked to poor prognosis, the researchers also identified a population of lymph node macrophages secreting PLA2G2D, which acted as an immune checkpoint that limited CD8+ T cell-mediated antitumor immunity. Blocking PLA2G2D reduced immunosuppression and enhanced antitumor immunity, resulting in reduced tumor growth, which could be further reduced in combination with anti-PD-1. PLA2G2D was also associated with poor prognosis in patient data.

ABSTRACT: Systemic anti-tumour immunity results from T cell priming in tumour-draining lymph nodes (TDLNs)(1-4). Although the suppression of T cells in tumours is well characterized(5-8), whether this occurs in TDLNs-and if so, through which mechanisms-remains poorly understood. Here, using imaging mass cytometry of TDLNs from patients with melanoma, we identify a spatial neighbourhood in the TDLN paracortex that is linked to the development of distant metastases. Targeted spatial transcriptomics of cells inside this neighbourhood revealed activated CD8(+) T cells engaging with myeloid cells that expressed high levels of the immunosuppressive secretory phospholipase PLA2G2D. PLA2G2D(+) myeloid cells were substantially more abundant in TDLNs than they were in primary tumours or metastases. Genetic loss-of-function or antibody-mediated inhibition of PLA2G2D reduced tumour growth markedly, and single-cell transcriptomics in melanoma-bearing mice revealed that expression of Pla2g2d is confined to lymph-node macrophages. Mechanistically, PLA2G2D directly suppressed the early proliferation of T cells in vitro, and inhibiting PLA2G2D resulted in an expansion of tumour-specific T cells in TDLNs, leading to an increase in these T cells in the circulation and subsequently in tumours. Notably, PLA2G2D and PD-1 act as non-redundant immune checkpoints, with combination treatment showing additive or synergistic efficacy in humanized mice treated with human-specific antibodies. Collectively, our in-depth spatial profiling identifies PLA2G2D as a TDLN-centred targetable immune checkpoint for cancer immunotherapy.

Author Info: (1) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam

Author Info: (1) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (2) Apeximmune Therapeutics, Burlingame, CA, USA. (3) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (4) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Laboratory of Immunoregulation and Mucosal Immunology, VIB-UGent Centre for Inflammation Research, Ghent, Belgium. (5) Apeximmune Therapeutics, Burlingame, CA, USA. Department of Pharmacological and Pharmaceutical Sciences, University of Houston, Houston, TX, USA. (6) Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Department of Surgical Oncology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (7) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (8) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (9) Department of Pathology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (10) Apeximmune Therapeutics, Burlingame, CA, USA. (11) Apeximmune Therapeutics, Burlingame, CA, USA. (12) Apeximmune Therapeutics, Burlingame, CA, USA. (13) Taipei Medical University, Taipei City, Taiwan. (14) Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. (15) Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. (16) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (17) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (18) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (19) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (20) Department of Pathology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (21) Department of Haematology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (22) Department of Pathology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (23) Department of Pathology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (24) Department of Molecular Cell Biology and Immunology, Amsterdam UMC, Amsterdam, The Netherlands. (25) Laboratory of Immunoregulation and Mucosal Immunology, VIB-UGent Centre for Inflammation Research, Ghent, Belgium. Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium. (26) Laboratory of Immunoregulation and Mucosal Immunology, VIB-UGent Centre for Inflammation Research, Ghent, Belgium. Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium. (27) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (28) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Laboratory of Immunoregulation and Mucosal Immunology, VIB-UGent Centre for Inflammation Research, Ghent, Belgium. Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium. (29) Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Department of Surgical Oncology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (30) Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Department of Surgical Oncology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (31) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. (32) Apeximmune Therapeutics, Burlingame, CA, USA. llee@apeximmune.com. (33) Apeximmune Therapeutics, Burlingame, CA, USA. klu@apeximmune.com. (34) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. r.stadhouders@erasmusmc.nl. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. r.stadhouders@erasmusmc.nl. (35) Department of Pulmonary Medicine, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. f.dammeijer@erasmusmc.nl. Erasmus MC Cancer Institute, Erasmus MC University Medical Centre, Rotterdam, The Netherlands. f.dammeijer@erasmusmc.nl.

Neutralizing the IL-12/IL-23 p40 subunit prevents immune checkpoint blockade toxicity without compromising antitumor efficacy Spotlight 

Groeneveldt et al. investigated mechanisms driving early-stage immune-related adverse events (irAEs) in patients with metastatic melanoma treated with combination ICB (anti-PD-1 and anti-CTLA-4). Proteomic analysis of serum identified an early, significant increase of p40, a subunit of IL-12/I-23, prior to the onset of clinically apparent irAEs, which was not associated with ICB efficacy. In mouse models that mimic irAEs in patients, p40 neutralization prevented ICB-induced toxicity, without compromising antitumor T cell immunity, including the establishment of tumor-specific responses, suggesting distinct mechanisms that can be uncoupled.

Contributed by Katherine Turner

Groeneveldt et al. investigated mechanisms driving early-stage immune-related adverse events (irAEs) in patients with metastatic melanoma treated with combination ICB (anti-PD-1 and anti-CTLA-4). Proteomic analysis of serum identified an early, significant increase of p40, a subunit of IL-12/I-23, prior to the onset of clinically apparent irAEs, which was not associated with ICB efficacy. In mouse models that mimic irAEs in patients, p40 neutralization prevented ICB-induced toxicity, without compromising antitumor T cell immunity, including the establishment of tumor-specific responses, suggesting distinct mechanisms that can be uncoupled.

Contributed by Katherine Turner

ABSTRACT: Immune checkpoint blockade (ICB) has markedly improved overall survival in various cancers, but is associated with severe and sometimes fatal immune-related adverse events (irAEs). Current management of irAEs involves discontinuation of ICB therapy and administration of immunosuppressive drugs, such as corticosteroids, which have been associated with decreased antitumor efficacy. Although irAE development is associated with ICB response, it is currently unknown whether their underlying mechanisms are shared or distinct. To identify early and targetable drivers of irAEs, we performed proteomic analyses on the serum of patients with cancer treated with anti-PD-1 and anti-CTLA-4 combination ICB. We identified a significantly increased concentration of p40, a subunit of IL-12/IL-23, shortly after the start of ICB but before the onset of clinically apparent irAEs. Importantly, increased p40 levels were not associated with ICB efficacy. Neutralizing p40 mitigated ICB-induced toxicity in various mouse models without impairing ICB-induced antitumor efficacy. In conclusion, we demonstrated that IL-12/IL-23p40 is a key mediator of ICB-induced toxicity while being redundant for ICB antitumor efficacy. This shows that the mechanisms underlying ICB toxicity and efficacy can be uncoupled and provides a rationale for p40 blockade in clinical trials with ICB treatment to prevent irAEs in patients.

Author Info: (1) Erasmus MC Rotterdam Netherlands. ROR: https://ror.org/018906e22 (2) Erasmus MC Cancer Institute Rotterdam, South-Holland Netherlands. ROR: https://ror.org/03r4m3349 (3) Erasmu

Author Info: (1) Erasmus MC Rotterdam Netherlands. ROR: https://ror.org/018906e22 (2) Erasmus MC Cancer Institute Rotterdam, South-Holland Netherlands. ROR: https://ror.org/03r4m3349 (3) Erasmus MC Netherlands. ROR: https://ror.org/018906e22 (4) Cancer Research UK Scotland Institute United Kingdom. ROR: https://ror.org/03pv69j64 (5) Erasmus MC Cancer Institute Rotterdam Netherlands. ROR: https://ror.org/03r4m3349 (6) Erasmus MC Cancer Institute Netherlands. ROR: https://ror.org/03r4m3349 (7) Erasmus MC Rotterdam Rotterdam Netherlands. (8) Erasmus MC Cancer Institute Netherlands. ROR: https://ror.org/03r4m3349 (9) Erasmus MC Rotterdam Rotterdam Netherlands. (10) Erasmus MC Cancer Institute Rotterdam Netherlands. ROR: https://ror.org/03r4m3349 (11) Erasmus MC Cancer Institute Rotterdam Netherlands. ROR: https://ror.org/03r4m3349 (12) Erasmus MC Rotterdam Netherlands. ROR: https://ror.org/018906e22 (13) University of Warsaw Poland. ROR: https://ror.org/039bjqg32 (14) Erasmus MC Rotterdam Netherlands. ROR: https://ror.org/018906e22 (15) Erasmus MC - Sophia Children's Hospital Rotterdam, South Holland Netherlands. ROR: https://ror.org/047afsm11 (16) Erasmus MC Rotterdam Netherlands. ROR: https://ror.org/018906e22 (17) Erasmus MC Rotterdam Rotterdam Netherlands. (18) Erasmus MC Cancer Institute Rotterdam Netherlands. ROR: https://ror.org/03r4m3349 (19) Erasmus MC Cancer Institute Rotterdam Netherlands. ROR: https://ror.org/03r4m3349

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.

A viral-based individualized neoantigen vaccine as adjuvant treatment in resected head and neck squamous cell carcinoma: a randomized Phase I trial

Spotlight 

Ottensmeier and Delord et al. report a randomized phase I trial of adjuvant TG4050, an individualized MVA-vectored neoantigen vaccine encoding up to 30 predicted neoantigens, in high-risk resected HNSCC. TG4050 was feasible and well tolerated, with no relapses among 16 immediately treated patients after 30 months median follow-up, compared to 3 of 16 who relapsed in the control arm. Neoantigen-specific T cell responses (median of 3 neoantigens per responder) occurred in 73.3% of treated patients and persisted for over one year. Vaccine-reactive CD8+ T cells were polyclonal, cytotoxic, and tissue-resident-like, comprising de novo and expanded pre-existing clones.

Contributed by Shishir Pant

Ottensmeier and Delord et al. report a randomized phase I trial of adjuvant TG4050, an individualized MVA-vectored neoantigen vaccine encoding up to 30 predicted neoantigens, in high-risk resected HNSCC. TG4050 was feasible and well tolerated, with no relapses among 16 immediately treated patients after 30 months median follow-up, compared to 3 of 16 who relapsed in the control arm. Neoantigen-specific T cell responses (median of 3 neoantigens per responder) occurred in 73.3% of treated patients and persisted for over one year. Vaccine-reactive CD8+ T cells were polyclonal, cytotoxic, and tissue-resident-like, comprising de novo and expanded pre-existing clones.

Contributed by Shishir Pant

ABSTRACT: In approximately one third of patients, resected head and neck squamous cell carcinoma will recur. We postulated that the induction of tumor neoantigen-specific T cell responses could prevent relapse. To this end, we developed TG4050, an individualized neoantigen therapeutic vaccine encoding up to 30 patient-specific predicted tumor neoantigens delivered by a Modified Vaccinia Ankara viral vector. We tested adjuvant TG4050 as single agent in a randomized phase I trial comparing treatment with TG4050 immediately after standard of care adjuvant therapy versus watchful waiting and treatment with TG4050 after recurrence (NCT04183166). The primary endpoint was safety, secondary endpoints included feasibility and efficacy, and immunogenicity was exploratory. TG4050 was well tolerated. Of 16 evaluable patients randomized the immediate treatment arm, none relapsed after a median follow-up of 30 months, while 3 of 16 relapsed in the control arm. T cell responses to vaccine neoantigens were detected in 73.3% of patients treated with TG4050 immediately, with a median of 3 neoantigens per responder. These responses were maintained throughout treatment and persisted for over one year after the last dose. Vaccine neoantigen-specific CD8+ T cells had an effector phenotype, displayed high expression of cytotoxic and tissue-resident markers, were polyclonal and comprised both de novo responses and amplification of pre-existing tumor-infiltrating T cell clones. Together, these translational data are consistent with the hypothesis in which single-agent delivery of TG4050 induces long-lasting tumor neoantigen-specific cytotoxic T cell responses that can prevent tumor recurrence.

Author Info:

Author Info:

Spatiotemporal multiomics uncover tumor ecosystem dynamics during metastatic colonization Spotlight 

Sun et al. performed multi-omics analysis across nine stages of mouse HCC lung metastatic colonization, with supporting human data, to map the co-evolution of disseminated tumor cells (DTCs) and host immune niches. A rare transient, quiescent subpopulation of Phgdhhigh DTCs survived neutrophil- and NK cell-mediated clearance. PHGDH-driven one-carbon metabolism increased S-adenosylmethionine and H3K27me3-mediated silencing of Ccl2 and Cxcl10 to establish an immune-scarce niche. Eventually, CX3CR1high interstitial macrophages accumulated, recruited immunosuppressive cells, and activated IGF1–IGF1R signaling to promote DTC outgrowth.

Contributed by Shishir Pant

Sun et al. performed multi-omics analysis across nine stages of mouse HCC lung metastatic colonization, with supporting human data, to map the co-evolution of disseminated tumor cells (DTCs) and host immune niches. A rare transient, quiescent subpopulation of Phgdhhigh DTCs survived neutrophil- and NK cell-mediated clearance. PHGDH-driven one-carbon metabolism increased S-adenosylmethionine and H3K27me3-mediated silencing of Ccl2 and Cxcl10 to establish an immune-scarce niche. Eventually, CX3CR1high interstitial macrophages accumulated, recruited immunosuppressive cells, and activated IGF1–IGF1R signaling to promote DTC outgrowth.

Contributed by Shishir Pant

ABSTRACT: The mechanisms underlying the interactions between disseminated tumor cells (DTCs) and their tissue microenvironment during metastatic colonization are currently poorly understood. We integrated multimodal single-cell and spatial profiling from liver cancer mouse models and human metastases to track the spatiotemporal dynamics of DTCs and their microenvironments from single-cell seeding to overt lung metastasis. We identified a residual population of quiescent Phgdh(high) DTCs that survived initial innate immune clearance and became transiently enriched in micrometastases. These cells shaped an immune-scarce microenvironment through PHGDH-dependent, H3K27me3-mediated epigenetic silencing of chemokine transcription, thereby promoting metastatic expansion. Cx3cr1(high) interstitial macrophages were also transiently enriched before DTC expansion, creating an immune-privileged niche for metastatic outgrowth by recruiting immunosuppressive cells. Inactivating the PHGDH-H3K27me3 axis in DTCs or depleting interstitial macrophages restored immune surveillance and inhibited metastatic colonization. These findings provide insights into the development of micrometastasis-targeting regimens.

Author Info: (1) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Insti

Author Info: (1) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (2) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. (3) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Ruijin Yangtze River Delta Health Institute, Wuxi Branch of Ruijin Hospital, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (4) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (5) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (6) School of Life Science and Technology, ShanghaiTech University, Shanghai, China. (7) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (8) Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. (9) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (10) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (11) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (12) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (13) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (14) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (15) BGI Research, Chongqing, China. Department of Neurology, Hubei Provincial Clinical Research Center for Parkinson's Disease, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China. (16) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (17) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (18) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (19) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (20) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (21) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (22) Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. (23) Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. First Hospital of Shanxi Medical University, Taiyuan, China. Molecular Imaging Precision Medical Collaborative Innovation Center, Shanxi Medical University, Taiyuan, China. (24) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (25) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. (26) Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. (27) BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (28) BGI Research, Hangzhou, China. (29) BGI Research, Chongqing, China. (30) BGI Research, Chongqing, China. (31) BGI Research, Chongqing, China. (32) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (33) BGI Research, Hangzhou, China. (34) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China. BGI, Shenzhen, China. (35) Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. Department of Oral and Maxillofacial Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Stomatology, Zhongshan Hospital Fudan University, Shanghai, China. (36) 3DC STAR Lab, BGI CELL, Shenzhen, China. Prince Fahad bin Sultan Research Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia. (37) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (38) BGI Research, Chongqing, China. JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing, China. (39) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. (40) School of Life Science and Technology, ShanghaiTech University, Shanghai, China. (41) Dunwill Med-Tech, Shanghai, China. (42) State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (43) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China. (44) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. BGI Research, Chongqing, China. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China. (45) Zhongshan-BGI Precision Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China. Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China.

Single-nucleus multimodal spatial transcriptomics reveals spatial colocalization of neoantigen-expressing tumor cells and cognate T cells Spotlight 

Nagler, Sud, and Ghannam et al. developed a droplet-based single-nucleus spatial transcriptomics platform, Slide-GoTags, that integrates genotyping, TCR sequencing, and snRNAseq from the same frozen section to map neoantigen-specific immunity in situ. Slide-GoTags showed spatial co-localization of neoantigen-expressing tumor cells and their cognate T cell clonotypes, and distinct ICB-driven spatial immune landscapes. In melanoma, ccRCC, GBM, and ovarian cancer, Slide-GoTags identified IFN-driven niches enriched in activated or progenitor T cell clonotypes, with three TCR-neoantigen pairs in immunologically hot tumors.

Contributed by Shishir Pant

Nagler, Sud, and Ghannam et al. developed a droplet-based single-nucleus spatial transcriptomics platform, Slide-GoTags, that integrates genotyping, TCR sequencing, and snRNAseq from the same frozen section to map neoantigen-specific immunity in situ. Slide-GoTags showed spatial co-localization of neoantigen-expressing tumor cells and their cognate T cell clonotypes, and distinct ICB-driven spatial immune landscapes. In melanoma, ccRCC, GBM, and ovarian cancer, Slide-GoTags identified IFN-driven niches enriched in activated or progenitor T cell clonotypes, with three TCR-neoantigen pairs in immunologically hot tumors.

Contributed by Shishir Pant

ABSTRACT: Improved methods to identify therapeutically relevant tumor neoantigens and their cognate T cells would aid the development of precision medicines for cancer. Here, we developed Slide-GoTags, a droplet-based single-nucleus spatial transcriptomics approach that characterizes neoantigen-specific immunity by integrating targeted transcript genotyping and T cell receptor (TCR) sequencing with single-nucleus RNA sequencing from the same slice of frozen tissue. Application of Slide-GoTags to mouse and human tumors revealed colocalization of clonally expanded, neoantigen-specific T cells with tumor cells expressing their cognate neoantigen. We also identified distinct spatial immune landscapes shaped by anti-PD1 or anti-CTLA4 blockade in mouse colorectal tumors. Across human tumor types, Slide-GoTags detected TCR-neoantigen interactions through spatial proximity and identified an enrichment of interferon-driven immunogenicity niches in immunologically 'hot' tumors compared to 'cold' tumors. These niches harbored three T cell clonotypes that colocalized with genotyped neoantigens, highlighting a spatially organized antitumor immune response. Collectively, Slide-GoTags establishes a framework for in situ mapping of T cell-tumor interactions directly from individual tissue.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (2) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (3) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Harvard/MIT MD-PhD Program and Harvard Immunology PhD Program, Harvard Medical School, Boston, MA, USA. (4) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (5) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (6) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (7) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (8) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (9) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (10) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (11) LEO Foundation Skin Immunology Research Center, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark. (12) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (13) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (14) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (15) Molecular Imaging Core (MIC), Dana-Farber Cancer Institute, Boston, MA, USA. (16) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (17) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (18) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (19) Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. (20) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (21) Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. (22) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (23) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (24) Department of Bio and Health Informatics, Technical University of Denmark, Copenhagen, Denmark. Center for Genomic Medicine, Copenhagen University Hospital, Copenhagen, Denmark. (25) Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA. (26) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (27) Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA. Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. (28) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (29) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (30) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (31) Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA. (32) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (33) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Harvard Medical School, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. catherine_wu@dfci.harvard.edu. Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA. catherine_wu@dfci.harvard.edu.

The efficacy of immunotherapy in glioma requires distal B cell responses in tumor-draining lymph nodes Spotlight 

Kim et al. showed that anti-CTLA-4 efficacy in orthotopic murine glioblastoma (GBM) models was abolished by B cell deficiency and tdLN removal. In WT mice, anti-CTLA-4 increased B cell numbers and GC reactions in tdLNs, but not in the TIME, and therapeutic efficacy required CD4+ T cell help to B cells. While maintaining tumor-reactive CD4+ T cell function in the TIME, in tdLNs, anti-CTLA-4 therapy boosted TFH cell differentiation to support GC B cell clonal expansion and generation of antibody-secreting cells producing tumor-binding class-switched IgG1 antibodies that promoted macrophage-mediated phagocytosis of glioma cells via Fc receptor signaling.

Contributed by Paula Hochman

Kim et al. showed that anti-CTLA-4 efficacy in orthotopic murine glioblastoma (GBM) models was abolished by B cell deficiency and tdLN removal. In WT mice, anti-CTLA-4 increased B cell numbers and GC reactions in tdLNs, but not in the TIME, and therapeutic efficacy required CD4+ T cell help to B cells. While maintaining tumor-reactive CD4+ T cell function in the TIME, in tdLNs, anti-CTLA-4 therapy boosted TFH cell differentiation to support GC B cell clonal expansion and generation of antibody-secreting cells producing tumor-binding class-switched IgG1 antibodies that promoted macrophage-mediated phagocytosis of glioma cells via Fc receptor signaling.

Contributed by Paula Hochman

ABSTRACT: Humoral immunity, mediated by B cells that mature in germinal centers in lymph nodes (LNs), is essential for adaptive immune responses, but its role in antitumor immunity and responses to immunotherapy remain unclear. Here, we show that activation of B cells in tumor-draining deep cervical LNs (dcLNs) is necessary for the efficacy of CTLA-4 (cytotoxic T lymphocyte-associated protein 4) immune checkpoint blockade in glioma in vivo. Anti-CTLA-4 therapy enhanced T follicular helper cell (T(FH) cell) expansion in dcLNs, leading to germinal center B cell responses, immunoglobulin G (IgG) class switching, and the generation of glioma-reactive antibodies. Glioma-bearing mice lacking antibody-secreting cells did not benefit from CTLA-4 blockade. Distally secreted IgG accumulated in the tumor microenvironment and promoted glioma cell phagocytosis in vivo. These findings define a B cell-dependent mechanism underlying CTLA-4-mediated control of glioma and provide a conceptual framework for future therapeutic strategies in tumor.

Author Info: (1) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (2) Laboratory of Host D

Author Info: (1) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (2) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (3) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (4) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (5) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (6) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (7) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (8) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (9) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (10) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (11) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (12) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Department of Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea. (13) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (14) Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. (15) Laboratory of Host Defenses, Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Regenerative Medical Research Institute (reMRI) for Aging-Related Diseases, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Generalizable AI predicts immunotherapy outcomes across cancers and treatments Spotlight 

Shen et al. developed COMPASS, an AI model that interprets pre-treatment tumor RNAseq data in the context of tumor-immune gene expression modules and maps them to predict ICB response. Trained on TCGA and cohort data, COMPASS was applicable across cohorts, indications, ICB drugs, and targets, with superior response prediction compared to established models or correlates (TMB, PD-L1). COMPASS also generated personalized “response maps” identifying potential resistance mechanisms; unexpected nonresponders (i.e. patients with an inflammatory TME) often had gene expression associated with angiogenesis, TGFβ, and B cell deficiency.

Contributed by Alex Najibi

Shen et al. developed COMPASS, an AI model that interprets pre-treatment tumor RNAseq data in the context of tumor-immune gene expression modules and maps them to predict ICB response. Trained on TCGA and cohort data, COMPASS was applicable across cohorts, indications, ICB drugs, and targets, with superior response prediction compared to established models or correlates (TMB, PD-L1). COMPASS also generated personalized “response maps” identifying potential resistance mechanisms; unexpected nonresponders (i.e. patients with an inflammatory TME) often had gene expression associated with angiogenesis, TGFβ, and B cell deficiency.

Contributed by Alex Najibi

ABSTRACT: Immune checkpoint inhibitors (ICIs) are a standard treatment across cancers, yet most patients do not respond, and existing biomarkers generalize poorly across tumor types and therapies. Here we present COMPASS, a pan-cancer foundation model that predicts immunotherapy response from bulk tumor transcriptomes using a concept bottleneck transformer. COMPASS encodes gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interaction and signaling pathways. Trained on 10,184 tumors across 33 cancer types, COMPASS achieves better average performance than 22 methods across 16 clinical cohorts spanning seven cancers and six ICIs, improving accuracy by 8.5% and area under the precision-recall curve by 15.7% on average across cohorts. COMPASS generalizes to cancer types and treatments not represented during fine-tuning and may inform indication selection and patient stratification. In survival analyses, patients classified by COMPASS as responders had longer overall survival (hazard ratio_=_4.7, P_<_0.0001). Personalized response maps connect gene expression to immune concepts, identifying programs associated with response and resistance; in immune-inflamed non-responders, COMPASS highlights programs including TGF_ signaling, endothelial exclusion, CD4(+) T cell dysfunction and B cell deficiency. COMPASS predicts immunotherapy response and provides hypothesis-generating mechanistic insight for trial design and translational studies.

Author Info: (1) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. (2) Department of Biome

Author Info: (1) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. (2) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (3) Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. (4) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (5) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (6) Roche Pharma Research and Early Development, Oncology Early Clinical Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland. (7) Computational Sciences Center of Excellence, F. Hoffmann-La Roche Ltd., Basel, Switzerland. daniel.marbach.dm1@roche.com. (8) Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. marinka@hms.harvard.edu. Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA. marinka@hms.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. marinka@hms.harvard.edu. Harvard Data Science Initiative, Cambridge, MA, USA. marinka@hms.harvard.edu.

Addressing Biases in Analysis of Time of Infusion: NCI/SWOG Trial S1404 Among Participants With High-Risk Resectable Melanoma Who Received Adjuvant Anti-PD-1 Therapy Spotlight 

In an analysis of a multi-center trial involving 628 patients with high-risk melanoma receiving adjuvant pembrolizumab, Othus et al. identified optimal time cut-points for the first infusion of 1:18 pm for recurrence-free survival and 3:48 pm for overall survival (OS). These findings, however, did not reach statistical significance regarding patient outcomes. Furthermore, the lack of threshold robustness was demonstrated when shifting the OS cut-point 30 minutes earlier, which yielded a hazard ratio of 0.98. Average infusion times trended earlier over the year, while appointments were on average later for patients living further from the treatment center.

Contributed by Ute Burkhardt

In an analysis of a multi-center trial involving 628 patients with high-risk melanoma receiving adjuvant pembrolizumab, Othus et al. identified optimal time cut-points for the first infusion of 1:18 pm for recurrence-free survival and 3:48 pm for overall survival (OS). These findings, however, did not reach statistical significance regarding patient outcomes. Furthermore, the lack of threshold robustness was demonstrated when shifting the OS cut-point 30 minutes earlier, which yielded a hazard ratio of 0.98. Average infusion times trended earlier over the year, while appointments were on average later for patients living further from the treatment center.

Contributed by Ute Burkhardt

PURPOSE: Multiple reports have suggested that receiving immunotherapy infusions earlier in the day is associated with improved outcomes, including longer overall survival (OS) and lower toxicity rates. However, the definition of early varies between publications. Reports also fail to account for confounding factors (including distance to infusion center), are subject to survivor bias (analyzing postbaseline factors at baseline), and do not adjust P values for multiple comparisons when evaluating multiple potential thresholds for early versus late time of day of infusion. METHODS: We analyzed a previously reported multicenter clinical trial evaluating pembrolizumab as adjuvant therapy for participants with resectable high-risk melanoma. Standard statistical methodologies that account for potential biasses were used to evaluate the association between time of day of infusion and clinical outcomes. RESULTS: A total of 628 participants received pembrolizumab and had time of first infusion recorded. The median age was 55 years, range, 20-82. Odds of infusion before 11:00 hours increased by 32% over 12 months of therapy (P = .013). Participants living further from their treating institution had later infusion times on average: odds of infusion before 11:00 decreased by 9% for each additional 50 miles (P = .017). The optimal cut point for first infusion time for OS was 15:48 with hazard ratio (HR) = 1.40; changing the cut point by 30 minutes earlier to 15:18 decreased HR to 0.98, indicating lack of robustness of the threshold. No significant association was identified between proportion of early infusions and outcomes in multivariable time-dependent Cox regression models. CONCLUSION: In this multicenter trial of adjuvant pembrolizumab for participants with high-risk melanoma, analyses that account for common sources of bias found no significant association between recurrence-free or OS and time of day of infusion.

Author Info: (1) Division of Public Health, Fred Hutchinson Cancer Center, Seattle WA. (2) Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH. (3)

Author Info: (1) Division of Public Health, Fred Hutchinson Cancer Center, Seattle WA. (2) Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH. (3) Department of Medicine, Dana Farber Cancer Institute, Boston, MA. Harvard Medical School, Boston, MA. (4) Medical Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH. (5) Medical Oncology, Providence Cancer Institute, Portland, OR. (6) Medical Oncology, Mass General Brigham Cancer Institute, Boston, MA. (7) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (8) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (9) Division of Hematology and Oncology, Robert H Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL. (10) Division of Clinical Oncology, University of Kansas Medical Center, Kansas City, KS. (11) Melanoma Program, University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA. (12) Melanoma Medical Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX. (13) Medical Oncology, McGill University Health Centre, Montreal, Canada. (14) Melanoma, Texas Oncology-Baylor Sammons Cancer Center, Dallas, TX. (15) Department of Medical Oncology, Stanford University School of Medicine, Palo Alto, CA. (16) Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. (17) Medical Oncology, Providence Cancer Institute, Portland, OR. (18) Department of Cutaneous Oncology, H Lee Moffitt Cancer Center, Tampa, FL. (19) Department of Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA. (20) Department of Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO.

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