Journal Articles

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

(1) Groeneveldt C (2) Vadgama D (3) Onrust-van Schoonhoven A (4) Whyte D (5) Hofman MM (6) Rozendaal NEM (7) van Nimwegen M (8) Eterman M (9) Dumoulin DW (10) Mathijssen RHJ (11) Joosse A (12) van der Veldt AAM (13) Boon L (14) Doukas M (15) Samsom JN (16) Stadhouders R (17) Dammeijer F (18) Aerts JGJV (19) Joosse ME

Cancer immunology research

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

(1) Groeneveldt C (2) Vadgama D (3) Onrust-van Schoonhoven A (4) Whyte D (5) Hofman MM (6) Rozendaal NEM (7) van Nimwegen M (8) Eterman M (9) Dumoulin DW (10) Mathijssen RHJ (11) Joosse A (12) van der Veldt AAM (13) Boon L (14) Doukas M (15) Samsom JN (16) Stadhouders R (17) Dammeijer F (18) Aerts JGJV (19) Joosse ME

Cancer immunology research

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

Citation: Cancer Immunol Res 2026 Aug 31 Epub08/31/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42671933

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Tumor immune microenvironment remodeling predicts response to checkpoint inhibitor therapy

(1) Lin Z (2) Chandra M (3) Srinivas N (4) Ugurel S (5) Becker JC (6) Aran D

Cancer cell

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

(1) Lin Z (2) Chandra M (3) Srinivas N (4) Ugurel S (5) Becker JC (6) Aran D

Cancer cell

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.

Citation: Cancer Cell 2026 Sep 1 Epub09/01/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42679812

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

Spotlight 

Christian Ottensmeier, Jean-Pierre Delord, Ana Lalanne, Camille Jamet, Anne-Laure Le Gac, Katell Bidet Huang, Benoît Grellier, Jules Deforges, Maud Brandely, Eric Quéméneur, Bérangère Bastien, Annette Tavernaro, Gisèle Lacoste, Valérie Schoettel, Clémentine Spring-Giusti, Nathalie Silvestre, Jean-Baptiste Marchand, Sylvain Robin, Emmanuelle Dochy, Maurizio Ceppi, Alessandro Riva, Naoko Yamagata, Per Brattas, Kazuhide Onoguchi, Yoshiko Yamashita, Hugues Fontenelle, Mariana Eggert Martinez, Oliver Baker, Terry Jones, Andrew Schache, Eliane Piaggio, Kaïdre Bendjama, Olivier Lantz & Christophe Le Tourneau

Nature Communications

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

Christian Ottensmeier, Jean-Pierre Delord, Ana Lalanne, Camille Jamet, Anne-Laure Le Gac, Katell Bidet Huang, Benoît Grellier, Jules Deforges, Maud Brandely, Eric Quéméneur, Bérangère Bastien, Annette Tavernaro, Gisèle Lacoste, Valérie Schoettel, Clémentine Spring-Giusti, Nathalie Silvestre, Jean-Baptiste Marchand, Sylvain Robin, Emmanuelle Dochy, Maurizio Ceppi, Alessandro Riva, Naoko Yamagata, Per Brattas, Kazuhide Onoguchi, Yoshiko Yamashita, Hugues Fontenelle, Mariana Eggert Martinez, Oliver Baker, Terry Jones, Andrew Schache, Eliane Piaggio, Kaïdre Bendjama, Olivier Lantz & Christophe Le Tourneau

Nature Communications

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:

Citation: Nat com 2026

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Spatiotemporal multiomics uncover tumor ecosystem dynamics during metastatic colonization Spotlight 

(1) Sun Y (2) Zhong Y (3) Liu S (4) Zhang Z (5) Wang C (6) Liu Y (7) Chen J (8) Guo W (9) Gu X (10) Rao K (11) Wang Z (12) Cao M (13) Wang Y (14) Huang W (15) Zou X (16) Chen X (17) Qiu S (18) Shi Y (19) Sun H (20) Huang X (21) Wang Y (22) Wang J (23) Wu Z (24) Tian R (25) Zhang Y (26) Gu J (27) Jiang M (28) Bai Y (29) Li G (30) Xie M (31) Xi F (32) Peng L (33) Liu S (34) Yang S (35) Zhang Y (36) Esteban MA (37) Jin X (38) Chen A (39) Wang J (40) Cang Y (41) Peng DH (42) Xu X (43) Zhou J (44) Wu L (45) Fan J

Science

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

(1) Sun Y (2) Zhong Y (3) Liu S (4) Zhang Z (5) Wang C (6) Liu Y (7) Chen J (8) Guo W (9) Gu X (10) Rao K (11) Wang Z (12) Cao M (13) Wang Y (14) Huang W (15) Zou X (16) Chen X (17) Qiu S (18) Shi Y (19) Sun H (20) Huang X (21) Wang Y (22) Wang J (23) Wu Z (24) Tian R (25) Zhang Y (26) Gu J (27) Jiang M (28) Bai Y (29) Li G (30) Xie M (31) Xi F (32) Peng L (33) Liu S (34) Yang S (35) Zhang Y (36) Esteban MA (37) Jin X (38) Chen A (39) Wang J (40) Cang Y (41) Peng DH (42) Xu X (43) Zhou J (44) Wu L (45) Fan J

Science

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.

Citation: Science 2026 Jul 30 393:eadz7928 Epub07/30/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42531396

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

(1) Nagler A (2) Sud A (3) Ghannam JY (4) Pomerance L (5) Robles-Oteiza C (6) Afeyan AB (7) Weir JA (8) Russell AJC (9) Lu WS (10) Van Orden M (11) Sonnenholzner A (12) Marrero GJ (13) Gong Q (14) Kumar V (15) Huang K (16) Tu C (17) Lin E (18) Shim B (19) De Oliveira GR (20) Sellars MC (21) Yoon CH (22) Reardon DA (23) Choueiri TK (24) Olsen LR (25) Signoretti S (26) Ott PA (27) Braun DA (28) Oliveira G (29) Li S (30) Livak KJ (31) Hacohen N (32) Chen F (33) Wu CJ

Nature biotechnology

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

(1) Nagler A (2) Sud A (3) Ghannam JY (4) Pomerance L (5) Robles-Oteiza C (6) Afeyan AB (7) Weir JA (8) Russell AJC (9) Lu WS (10) Van Orden M (11) Sonnenholzner A (12) Marrero GJ (13) Gong Q (14) Kumar V (15) Huang K (16) Tu C (17) Lin E (18) Shim B (19) De Oliveira GR (20) Sellars MC (21) Yoon CH (22) Reardon DA (23) Choueiri TK (24) Olsen LR (25) Signoretti S (26) Ott PA (27) Braun DA (28) Oliveira G (29) Li S (30) Livak KJ (31) Hacohen N (32) Chen F (33) Wu CJ

Nature biotechnology

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.

Citation: Nat Biotechnol 2026 Jul 22 Epub07/22/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42486969

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

(1) Kim Y (2) Kang I (3) Kang BH (4) Park WH (5) Kim CW (6) Kim HJ (7) La J (8) Kwon MS (9) Park SH (10) Im SH (11) Kim HC (12) Ku KB (13) Kim M (14) Oh JE (15) Lee HK

Science immunology

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

(1) Kim Y (2) Kang I (3) Kang BH (4) Park WH (5) Kim CW (6) Kim HJ (7) La J (8) Kwon MS (9) Park SH (10) Im SH (11) Kim HC (12) Ku KB (13) Kim M (14) Oh JE (15) Lee HK

Science immunology

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.

Citation: Sci Immunol 2026 Jul 10 11:eadz2494 Epub07/10/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42430444

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Generalizable AI predicts immunotherapy outcomes across cancers and treatments Spotlight 

(1) Shen W (2) Moon I (3) Nguyen TH (4) Li MM (5) Huang Y (6) Nair N (7) Marbach D (8) Zitnik M

Nature medicine

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

(1) Shen W (2) Moon I (3) Nguyen TH (4) Li MM (5) Huang Y (6) Nair N (7) Marbach D (8) Zitnik M

Nature medicine

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.

Citation: Nat Med 2026 Jul 3 Epub07/03/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42399673

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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 

(1) Othus M (2) Truong TG (3) Sharon E (4) Kendra K (5) Grossmann K (6) Buchbinder E (7) Khushalani NI (8) Eroglu Z (9) Chandra S (10) Doolittle GC (11) Kirkwood JM (12) Ikeguchi A (13) Mihalcioiu C (14) Cowey CL (15) Reddy SA (16) Johnson DB (17) Taylor M (18) Sondak VK (19) Ribas A (20) Patel SP

JCO oncology practice | Free PMC Article

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

(1) Othus M (2) Truong TG (3) Sharon E (4) Kendra K (5) Grossmann K (6) Buchbinder E (7) Khushalani NI (8) Eroglu Z (9) Chandra S (10) Doolittle GC (11) Kirkwood JM (12) Ikeguchi A (13) Mihalcioiu C (14) Cowey CL (15) Reddy SA (16) Johnson DB (17) Taylor M (18) Sondak VK (19) Ribas A (20) Patel SP

JCO oncology practice | Free PMC Article

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.

Citation: JCO Oncol Pract 2026 Jun 23 OP2501413 Epub06/23/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42335437

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Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling Spotlight 

(1) Suissa D (2) Fidelle M (3) Reich E (4) Pham TN (5) Thomas S (6) Bjšrk JR (7) Liu P (8) Zhao L (9) Kitaoka K (10) Piard E (11) Lebhar I (12) Tian AL (13) Thelemaque C (14) Alves Costa Silva C (15) Deutsch E (16) Loriot Y (17) Segata N (18) Piccinno G (19) Hospers GAP (20) Maleki Vareki S (21) Silverman MS (22) Lenehan JG (23) Bataille V (24) Boulate D (25) Kuznetsova T (26) Weersma RK (27) Messaoudene M (28) Durand S (29) van der Aalst CM (30) de Koning HJ (31) Schuler-Thurner B (32) de Vries IJM (33) Rafie E (34) Saliby RM (35) Machaalani M (36) Haferkamp S (37) Schilling B (38) Porcari S (39) Ciccarese C (40) Iacovelli R (41) Cremolini C (42) Choueiri TK (43) Elkrief A (44) Kroemer G (45) Heinzerling L (46) Chamoto K (47) Ianiro G (48) Routy B (49) Derosa L (50) Paragios N (51) Zitvogel L

Nature medicine

Suissa and Fidelle et al. performed targeted metabolomics to 4,336 plasma samples from 1,714 ICI-treated patients across 16 cohorts and trained an ML model that predicted 12‑month PFS, with histidine as a favorable marker and long-chain fatty acids and succinate associated with poor outcome. Histidine supplementation promoted mitochondrial FAO and regulated T cell exhaustion, enhancing ICI-induced antitumor immunity in fibrosarcoma and melanoma models. Histidine-rich diet was associated with favorable PFS in patients without dysbiosis-associated histidine catabolism, and fecal histidine levels inversely correlated with severe irAEs.

Contributed by Shishir Pant

(1) Suissa D (2) Fidelle M (3) Reich E (4) Pham TN (5) Thomas S (6) Bjšrk JR (7) Liu P (8) Zhao L (9) Kitaoka K (10) Piard E (11) Lebhar I (12) Tian AL (13) Thelemaque C (14) Alves Costa Silva C (15) Deutsch E (16) Loriot Y (17) Segata N (18) Piccinno G (19) Hospers GAP (20) Maleki Vareki S (21) Silverman MS (22) Lenehan JG (23) Bataille V (24) Boulate D (25) Kuznetsova T (26) Weersma RK (27) Messaoudene M (28) Durand S (29) van der Aalst CM (30) de Koning HJ (31) Schuler-Thurner B (32) de Vries IJM (33) Rafie E (34) Saliby RM (35) Machaalani M (36) Haferkamp S (37) Schilling B (38) Porcari S (39) Ciccarese C (40) Iacovelli R (41) Cremolini C (42) Choueiri TK (43) Elkrief A (44) Kroemer G (45) Heinzerling L (46) Chamoto K (47) Ianiro G (48) Routy B (49) Derosa L (50) Paragios N (51) Zitvogel L

Nature medicine

Suissa and Fidelle et al. performed targeted metabolomics to 4,336 plasma samples from 1,714 ICI-treated patients across 16 cohorts and trained an ML model that predicted 12‑month PFS, with histidine as a favorable marker and long-chain fatty acids and succinate associated with poor outcome. Histidine supplementation promoted mitochondrial FAO and regulated T cell exhaustion, enhancing ICI-induced antitumor immunity in fibrosarcoma and melanoma models. Histidine-rich diet was associated with favorable PFS in patients without dysbiosis-associated histidine catabolism, and fecal histidine levels inversely correlated with severe irAEs.

Contributed by Shishir Pant

ABSTRACT: Immune-checkpoint inhibitors benefit a subset of patients with advanced cancer, and the metabolic determinants of response remain unclear. Here, using targeted metabolomics and metagenomics, we profiled 4,336 plasma samples from 1,714 patients across five tumor types and 16 cohorts spanning Europe and North America, longitudinally sampled during five immune-checkpoint inhibitor-based treatment modalities, including fecal microbiota transplantation. A multimodal machine-learning framework integrating 154 metabolites with clinical variables identified five metabolites, age, body mass index and renal function as predictors of 12-month progression-free survival. The model achieved areas under the curve of 0.88 in training and 0.73 in validation cohorts of 105 and 30 patients, respectively and generalized across seven external cohorts. Histidine was a favorable prognostic feature of survival, whereas long-chain fatty acids and succinate were negatively associated with outcome. Histidine supplementation enhanced antitumor immunity in mice. Histidine-rich diets improved progression-free survival in patients lacking dysbiotic microbiome signatures associated with histidine catabolism.

Author Info: (1) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (2) UniversitŽ Paris-Saclay,

Author Info: (1) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (2) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (3) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (4) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (5) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (6) Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (7) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (8) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (9) Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. (10) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (11) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (12) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (13) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. (14) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Oncoclinicas&Co - Medica Scientia Innovation Research (MEDSIR), Sao Paulo, Brazil. Gonalo Moniz Institute, Fiocruz, Salvador, Brazil. Federal University of Bahia, Salvador, Brazil. (15) Department of Radiation Oncology, Gustave Roussy, UniversitŽ Paris-Saclay, INSERM U1355, RHU LySAIRI, Villejuif, France. (16) Department of Therapeutic Innovation and Early Trials (DITEP), INSERM U981, Gustave Roussy, Villejuif, France. (17) Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy. (18) Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy. (19) Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (20) Verspeeten Family Cancer Centre, London Health Sciences Research Institute, London, Ontario, Canada. Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada. Department of Oncology, Division of Experimental Oncology, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. (21) Lawson Health Research Institute, London, Ontario, Canada. Department of Microbiology & Immunology, Western University, London, Ontario, Canada. Department of Medicine, Division of Infectious Diseases, Western University, London, Ontario, Canada. Division of Infectious Diseases, St Joseph's Health Care, London, Ontario, Canada. (22) Verspeeten Family Cancer Centre, London Health Sciences Research Institute, London, Ontario, Canada. Department of Oncology, Western University, London, Ontario, Canada. (23) Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. Department of Dermatology, Mount Vernon Cancer Centre, Northwood, UK. Department of Dermatology, Hemel Hempstead Hospital, West Hertfordshire NHS Trust, Hemel Hempstead, UK. (24) Department of Thoracic Surgery, H™pital-Nord-APHM, Aix-Marseille University, Marseille, France. H™pital Marie Lannelongue, GHPSJ, Le Plessis-Robinson, France. (25) Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium. (26) Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands. (27) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. (28) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. (29) Department of Public Health, Erasmus Medical Centre - University Medical Centre Rotterdam, Rotterdam, The Netherlands. (30) Department of Public Health, Erasmus Medical Centre - University Medical Centre Rotterdam, Rotterdam, The Netherlands. (31) Department of Dermatology, Friedrich-Alexander-UniversitŠt Erlangen-NŸrnberg (FAU), UniversitŠtsklinikum Erlangen, Erlangen, Germany. Bavarian Cancer Research Center (BZKF), Erlangen, Germany. (32) Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands. (33) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (34) Dana-Farber Cancer Institute, Boston, MA, USA. Yale School of Medicine, New Haven, CT, USA. (35) Dana-Farber Cancer Institute, Boston, MA, USA. (36) Bavarian Cancer Research Center (BZKF), Erlangen, Germany. Department of Dermatology, University Hospital Regensburg, Regensburg, Germany. (37) Department of Dermatology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany. (38) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC CEMAD Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli IRCCS, Rome, Italy. (39) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC Oncologia Medica, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. (40) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC Oncologia Medica, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. (41) Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy. (42) Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (43) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (44) INSERM U1138 - Metabolism, Cancer & Immunity, ƒquipe LabellisŽe par la Ligue Contre le Cancer, Centre de Recherche des Cordeliers, UniversitŽ Paris CitŽ, Sorbonne UniversitŽ, Paris, France. UniversitŽ Paris-Saclay, INSERM US23 AMMICa, Metabolomic Platform, Gustave Roussy, Villejuif, France. Institut du Cancer Paris CARPEM, Department of Biology, H™pital EuropŽen Georges Pompidou, AP-HP, Paris, France. (45) Department of Dermatology, Friedrich-Alexander-UniversitŠt Erlangen-NŸrnberg (FAU), UniversitŠtsklinikum Erlangen, Erlangen, Germany. Bavarian Cancer Research Center (BZKF), Erlangen, Germany. Department of Dermatology and Allergy, LMU University Hospital LMU Munich, Munich, Germany. (46) Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. Division of Cancer Immune Regulation, Center for Cancer Immunotherapy and Immunobiology (CCII), Graduate School of Medicine, Kyoto University, Kyoto, Japan. (47) Department of Translational Medicine and Surgery, Universitˆ Cattolica del Sacro Cuore, Facoltˆ di Medicina e Chirurgia, Rome, Italy. Department of Medical and Surgical Sciences, UOC CEMAD Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli IRCCS, Rome, Italy. (48) Centre de Recherche du Centre Hospitalier de l'UniversitŽ de MontrŽal (CRCHUM), Axe Cancer, Montreal, Quebec, Canada. Hemato-Oncology Division, Centre Hospitalier de l'UniversitŽ de MontrŽal (CHUM), Montreal, Quebec, Canada. (49) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Department of Medical Oncology, Gustave Roussy, Villejuif, France. (50) MICS Laboratory, CentraleSupŽlec, UniversitŽ Paris-Saclay, Gif-sur-Yvette, France. nikos.paragios@centralesupelec.fr. TheraPanacea, Paris, France. nikos.paragios@centralesupelec.fr. (51) UniversitŽ Paris-Saclay, Gustave Roussy, ClinicObiome, Inserm UMR1367, Microbiota and Mucosal Immunity for Cancer Immunotherapy, Villejuif, France. Laurence.zitvogel@gustaveroussy.fr.

Citation: Nat Med 2026 Jun 25 Epub06/25/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42350644

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Tumor suppressor genotype influences the extent and mode of immunosurveillance in lung cancer Spotlight 

(1) Adler KM (2) Xu H (3) Gladstein AC (4) Irizarry-Negron VM (5) Robertson MR (6) Doerig KR (7) Petrov DA (8) Winslow MM (9) Feldser DM

Nature communications

Using genetically engineered conditional mouse models and lentiviral-mediated somatic gene inactivation, Adler and Xu et al. developed models that allowed them to quantify immunoediting by evaluating fixed neoantigen expression against genotypic tumor backgrounds defined by common driver mutations and different tumor suppressor genes. While genetic features promoting tumor proliferation generally correlated with increased sensitivity to immunosurveillance, different genotypes differentially affected immune cell recruitment, selection of tumor cells with neoantigen silencing, tumor growth, and mechanisms of immune evasion.

Contributed by Lauren Hitchings

(1) Adler KM (2) Xu H (3) Gladstein AC (4) Irizarry-Negron VM (5) Robertson MR (6) Doerig KR (7) Petrov DA (8) Winslow MM (9) Feldser DM

Nature communications

Using genetically engineered conditional mouse models and lentiviral-mediated somatic gene inactivation, Adler and Xu et al. developed models that allowed them to quantify immunoediting by evaluating fixed neoantigen expression against genotypic tumor backgrounds defined by common driver mutations and different tumor suppressor genes. While genetic features promoting tumor proliferation generally correlated with increased sensitivity to immunosurveillance, different genotypes differentially affected immune cell recruitment, selection of tumor cells with neoantigen silencing, tumor growth, and mechanisms of immune evasion.

Contributed by Lauren Hitchings

ABSTRACT: The impact of cancer driving mutations on immunosurveillance throughout tumor development remains poorly understood. To better understand the contribution of tumor genotype to immunosurveillance, we generated and validated lentiviral-based vectors that create increasingly immunogenic neoantigens. This vector system is compatible with autochthonous Cre-regulated cancer models, CRISPR/Cas9-mediated somatic genome editing, and tumor barcoding. Here, we show that in the context of oncogenic KRAS-driven lung cancer and strong neoantigen expression, tumor suppressor genotype dictates the degree of immune cell recruitment, positive selection of tumors with neoantigen silencing, and tumor outgrowth. By quantifying the impact of 11 commonly inactivated tumor suppressor genes on tumor growth across neoantigenic contexts, we show that the growth-promoting effects of tumor suppressor gene inactivation correlate with increasing sensitivity to immunosurveillance. Importantly, some genotypes also dramatically changed sensitivity to immunosurveillance independently of their growth-promoting effects. We propose a model of immunoediting in which tumor suppressor gene inactivation works in tandem with neoantigen expression to shape tumor immunosurveillance and immunoediting such that the same neoantigens uniquely modulate tumor immunoediting depending on the genetic context.

Author Info: (1) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medi

Author Info: (1) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (2) Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. (3) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (4) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (5) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (6) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (7) Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. (8) Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. Department of Biology, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA. mwinslow@stanford.edu. (9) Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu. Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dfeldser@upenn.edu.

Citation: Nat Commun 2026 Jun 15 Epub06/15/2026

Link to PUBMED: http://www.ncbi.nlm.nih.gov/pubmed/42297823

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