Journal Articles

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer Spotlight 

Azimazade et al. developed an explainable machine learning (XML) pipeline to study associations between clinical outcomes and in silico estimated cell types within the TIME of over 5,000 METABRIC and TCGA samples from patients with breast cancer. In estrogen receptor-positive samples, macrophages correlated positively with pathological complete responses after neoadjuvant chemotherapy, but negatively with relapse-free survival. Imaging mass cytometry and scRNAseq data demonstrated that HLA-ABC+ macrophages accumulated in the vicinity of HLA-ABChi epithelial cells and were associated with Tregs and TEX cells.

Contributed by Ute Burkhardt

Azimazade et al. developed an explainable machine learning (XML) pipeline to study associations between clinical outcomes and in silico estimated cell types within the TIME of over 5,000 METABRIC and TCGA samples from patients with breast cancer. In estrogen receptor-positive samples, macrophages correlated positively with pathological complete responses after neoadjuvant chemotherapy, but negatively with relapse-free survival. Imaging mass cytometry and scRNAseq data demonstrated that HLA-ABC+ macrophages accumulated in the vicinity of HLA-ABChi epithelial cells and were associated with Tregs and TEX cells.

Contributed by Ute Burkhardt

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis remain poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devise an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying this pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we create a comprehensive catalog of the TME's role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) data analysis, we find that macrophages tend to accumulate in the vicinity of HLA-ABC(hi) epithelial cells as their frequency increases in tumor tissues and that they also express elevated levels of HLA-ABC protein. In both IMC and single-cell RNA sequencing (scRNA-seq) data, we uncover a significant association between these HLA-ABC(hi) macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicts interactions between HLA-ABC(hi) macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, this work enhances the characterization of macrophage-associated immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

Author Info: (1) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. younessazimzade@gmail.com. (2) Department of Tumor Biology, Institute for Cancer Research, Div

Author Info: (1) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. younessazimzade@gmail.com. (2) Department of Tumor Biology, Institute for Cancer Research, Division of Cancer Medicine, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway. (3) Department of Medical Genetics, Oslo University Hospital, University of Oslo, Oslo, Norway. (4) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. arnoldo.frigessi@medisin.uio.no. Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway. arnoldo.frigessi@medisin.uio.no. (5) Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway. a.k.luque@medisin.uio.no. Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway. a.k.luque@medisin.uio.no.

Targeting CCR1 remodels the tumor microenvironment and relieves immune suppression in pancreatic cancer Featured  

Evaluating the role of CCR1 in pancreatic cancer, Zhang et al. used KC and KPC mouse tumor models, and found while elimination of CCR1 did not limit tumor formation, it delayed progression of active disease, resulting in prolonged survival. CCR1 was mainly expressed by macrophages and granulocytes, but its deletion induced TIME remodeling that affected fibroblasts and increased CD8+ T cell accumulation, but not activation. CCR1 inhibition showed synergy in combination with targeting of other immunosuppressive mechanisms, though there was still room to improve antitumor efficacy in this highly resistant tumor setting.

Evaluating the role of CCR1 in pancreatic cancer, Zhang et al. used KC and KPC mouse tumor models, and found while elimination of CCR1 did not limit tumor formation, it delayed progression of active disease, resulting in prolonged survival. CCR1 was mainly expressed by macrophages and granulocytes, but its deletion induced TIME remodeling that affected fibroblasts and increased CD8+ T cell accumulation, but not activation. CCR1 inhibition showed synergy in combination with targeting of other immunosuppressive mechanisms, though there was still room to improve antitumor efficacy in this highly resistant tumor setting.

ABSTRACT: A hallmark of pancreatic cancer is an extensive fibroinflammatory stroma. Myeloid cells, including abundant macrophages, are a prevalent cellular component of the pancreatic cancer microenvironment and a key driver of immunosuppression. Identifying mechanisms of myeloid-cell driven immunosuppression is thus key to developing therapeutic approaches. Harnessing single-cell RNA sequencing data from human and murine tumors, we determined that tumor infiltrating myeloid cells (including macrophages and granulocytes) have elevated expression of C-C motif chemokine receptor 1 (CCR1). To determine the functional role of CCR1, we generated oncogenic KRAS based genetically engineered mouse models of pancreatic cancer, with or without addition of a mutant form of the tumor suppressor Trp53 (KC and KPC, respectively), lacking CCR1 expression. CCR1 inactivation did not affect formation of early lesions, but delayed progression to cancer and resulted in prolonged survival. In these mice, macrophages lacking CCR1 had reduced expression of the immunosuppressive marker Arginase 1. Loss of CCR1 also profoundly shifted the prevalent fibroblast population, inducing a pancreatic stellate cell-like phenotype. In two independent syngeneic orthotopic models, ablation or pharmacologic inhibition of CCR1 reduced tumor growth and increased CD8+ T cell cytotoxic activity, sensitizing tumors to immunotherapy. Our data show that CCR1-expressing myeloid cells promote pancreatic cancer growth through modulation of the immune microenvironment and fibroblasts, indicating that CCR1 might be a suitable target for combination therapy.

Author Info: (1) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (2) University of Michigan-Ann Arbor Ann Arbor, MI United States. (3) University of

Author Info: (1) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (2) University of Michigan-Ann Arbor Ann Arbor, MI United States. (3) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (4) University of Michigan Medical Schooligan United States. (5) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (6) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (7) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (8) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (9) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (10) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (11) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (12) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (13) University of Maryland, Baltimore Baltimore United States. ROR: https://ror.org/04rq5mt64 (14) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (15) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (16) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (17) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (18) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (19) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (20) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (21) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (22) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (23) University of Michigan-Ann Arbor Ann Arbor United States. ROR: https://ror.org/00jmfr291 (24) University of Michigan-Ann Arbor United States. ROR: https://ror.org/00jmfr291 (25) Cornell University Ithaca United States. ROR: https://ror.org/05bnh6r87 (26) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (27) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (28) University of Michigan-Ann Arbor Ann Arbor, Michigan United States. ROR: https://ror.org/00jmfr291 (29) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (30) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (31) Cedars-Sinai Medical Center Los Angeles, CA United States. ROR: https://ror.org/02pammg90 (32) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291 (33) University of Michigan-Ann Arbor Ann Arbor, MI United States. ROR: https://ror.org/00jmfr291

Pan-cancer spatial atlas of tertiary lymphoid structures Spotlight 

Cho et al. integrated whole-section (WS) spatial transcriptomics across 12 cancer types to construct a pan-cancer tertiary lymphoid structure (TLS) atlas. TLSs spanned early, primary, and secondary maturation states with distinct spatial niches and immune organization. Tumor regions proximal to intratumoral TLSs showed enriched antigen-presentation and IFN-response programs, and reduced proliferative and EMT signatures. An AI framework trained on whole-slide H&E images classified TLS maturation and maturation-aware TLS composite scores, which stratified survival and treatment response, outperforming conventional TLS metrics.

Contributed by Shishir Pant

Cho et al. integrated whole-section (WS) spatial transcriptomics across 12 cancer types to construct a pan-cancer tertiary lymphoid structure (TLS) atlas. TLSs spanned early, primary, and secondary maturation states with distinct spatial niches and immune organization. Tumor regions proximal to intratumoral TLSs showed enriched antigen-presentation and IFN-response programs, and reduced proliferative and EMT signatures. An AI framework trained on whole-slide H&E images classified TLS maturation and maturation-aware TLS composite scores, which stratified survival and treatment response, outperforming conventional TLS metrics.

Contributed by Shishir Pant

ABSTRACT: Tertiary lymphoid structures (TLSs) are critical regulators of antitumor immunity, yet their spatial organization, maturation, and clinical relevance remain incompletely defined across cancers. We analyzed spatial transcriptomics spanning 12 cancer types to construct a pan-cancer TLS atlas and characterized TLS spatial architecture and maturation states. TLS maturation was accompanied by coordinated remodeling of distinct niche cell populations and distance-dependent gradients in tumor programs, orthogonally supported by ultrahigh-plex single-cell spatial profiling. To enable scalable TLS profiling, we trained an artificial intelligence framework that predicts TLS maturation states directly from hematoxylin and eosin-stained images and evaluated it across TCGA and independent therapy cohorts. We further derived a maturation-aware composite score capturing intratumoral TLS state composition, which robustly stratifies patients across cancer and treatment contexts, outperforming conventional TLS metrics.

Author Info: (1) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Genomic Medicine, The University of Texas MD Anderson Can

Author Info: (1) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (3) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (4) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (5) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. (6) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (7) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (8) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (9) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (10) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (11) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (12) Laura and Isaac Perlmutter Cancer Center, Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA. (13) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (14) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (15) Department of Biostatistics, The University of North Carolina, Chapel Hill, NC, USA. Department of Genetics, The University of North Carolina, Chapel Hill, NC, USA. (16) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (17) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (18) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (19) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (20) Therapeutics Discovery Division, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (21) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (22) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (23) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (24) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (25) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (26) Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (27) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (28) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (29) Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (30) Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (31) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. (32) Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (33) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (34) Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (35) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (36) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (37) Centre de Recherche des Cordeliers, Sorbonne UniversitŽ, INSERM, Universite Paris Cite, Equipe labellisŽe Ligue Contre le Cancer, Paris, France. (38) Laura and Isaac Perlmutter Cancer Center, Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA. Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA. (39) The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center; Houston, TX, USA. (40) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (41) Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (42) Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences; Houston, TX, USA. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Center for Cellular Language Intelligence, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Differential assembly of mouse and human tumor microenvironments Spotlight 

Courau et al. profiled immune landscapes of 15 common mouse tumor models alongside human datasets. Most murine TIMEs resembled a minority subset of macrophage-rich, poorly infiltrated human tumors. Cross-species analysis showed species-specific biases in chemokine networks (including reduced CCR2/CCR5 and altered CXCL13 in mice) and altered T and myeloid cell frequencies, while conserved cell-type specific gene expression programs emerged as discriminatory. An IFN-responsive myeloid–CD8+ T cell cytotoxicity module was conserved across tumor types, and predicted clinical outcome in humans.

Contributed by Shishir Pant

Courau et al. profiled immune landscapes of 15 common mouse tumor models alongside human datasets. Most murine TIMEs resembled a minority subset of macrophage-rich, poorly infiltrated human tumors. Cross-species analysis showed species-specific biases in chemokine networks (including reduced CCR2/CCR5 and altered CXCL13 in mice) and altered T and myeloid cell frequencies, while conserved cell-type specific gene expression programs emerged as discriminatory. An IFN-responsive myeloid–CD8+ T cell cytotoxicity module was conserved across tumor types, and predicted clinical outcome in humans.

Contributed by Shishir Pant

ABSTRACT: Mouse models are frequently used to develop treatments for human cancer. However, the degree to which their tumor microenvironments (TMEs) are synonymously assembled is particularly poorly characterized. Through systematic immunoprofiling of 15 commonly used mouse models, we found that most murine TMEs recapitulate the composition of poorly infiltrated human tumors, extensively biased toward high macrophage densities. We discovered substantial species-specific biases of chemokine expression networks known to drive TMEs assembly, together with discoordinated frequencies of T and myeloid cell subtypes. Even with variable alignment, conserved cell-type-specific gene expression programs emerged across species and cohorts. Dissecting the coordinated T cell-myeloid gene expression programs revealed a conserved axis between interferon-responsive myeloid states and ongoing T cell cytotoxicity that transcends tissue of origin and predicts clinical outcome. Collectively, this work provides a practical atlas outlining both the hazards and opportunities of using mice to model human cancer.

Author Info: (1) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. tristan.courau@u

Author Info: (1) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. CoLabs, UCSF, San Francisco, CA, USA. tristan.courau@ucsf.edu. (2) CoLabs, UCSF, San Francisco, CA, USA. (3) ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (4) CoLabs, UCSF, San Francisco, CA, USA. (5) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (6) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (7) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (8) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (9) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (10) CoLabs, UCSF, San Francisco, CA, USA. (11) CoLabs, UCSF, San Francisco, CA, USA. (12) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. (13) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. (14) CoLabs, UCSF, San Francisco, CA, USA. (15) CoLabs, UCSF, San Francisco, CA, USA. (16) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (17) Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, USA. (18) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (19) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (20) The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA. (21) The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. (22) CoLabs, UCSF, San Francisco, CA, USA. Department of Medicine, Division of Rheumatology, UCSF, San Francisco, CA, USA. (23) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. CoLabs, UCSF, San Francisco, CA, USA. Department of Medicine, Division of Gastroenterology, UCSF, San Francisco, CA, USA. (24) Department of Pathology and ImmunoX Initiative, UCSF, San Francisco, CA, USA. max.krummel@ucsf.edu. ImmunoProfiler Initiative, UCSF, San Francisco, CA, USA. max.krummel@ucsf.edu.

Loss of the autoimmune risk gene TREX1 reveals a convergence of mechanisms promoting immune tolerance loss and antitumor immunity Spotlight 

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

As certain irAEs correlate with clinical efficacy following checkpoint inhibitor therapy, Lim and Williams et al. investigated the relationship between autoimmunity and antitumor immunity. Loss of TREX1, an autoimmune risk gene and key negative regulator of the STING and type I IFN pathways promoted antitumor immunity in mice, and shared pathways with successful cancer immunotherapy. Like in PDCD1-/- and CTLA4-/- mice, constitutive TREX1 loss resulted in multiorgan CD8+ T cell influx, autoimmunity, and myocarditis. Conditional systemic TREX1 ablation was well tolerated and promoted effective CD8+ T cell-driven antitumor immunity, suggesting a new opportunity for immunotherapy.

Contributed by Katherine Turner

ABSTRACT: Checkpoint inhibitors targeting PD-1 and CTLA-4 have transformed cancer therapy. Both are genetically associated with autoimmune disorders. Moreover, certain immune-related adverse events and autoimmune risk variants are linked to the clinical efficacy of checkpoint inhibition. These associations suggest common principles governing successful cancer immunotherapy and autoimmune susceptibility. Here, we show that ablation of the cytosolic DNA exonuclease TREX1 predisposes mice to autoimmunity while promoting robust antitumor immunity. Constitutive TREX1 loss leads to early onset autoimmunity, characterized by multiorgan CD8+ T cell infiltration, myocarditis, and Sjgren's syndrome-like disease. In contrast, induced systemic TREX1 ablation is well tolerated and promotes effective CD8+ T cell-driven antitumor immunity. Detailed phenotypic studies revealed a notable overlap between productive antitumor and pathogenic autoimmune CD8+ T cell responses. Collectively, we provide mechanistic evidence for interrelated mechanisms underlying autoimmunity and successful cancer immunotherapy, uncover key parallels between adaptive T cell and innate immune checkpoints, and suggest that targeting autoimmune risk genes represents a promising future avenue for cancer immunotherapy.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Author Info: 1Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Tumors hijack immune-privileging regulons via distinct cell types to confer T cell desertion and immunotherapy resistance across various cancers Spotlight 

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

Lawal et al. identified an immune-privileging regulon signature (IMPREG) from tumor samples of patients who were non-responsive to ICB. IMPREG mirrors transcriptional programs of immune-privileged organs. Transcriptomics revealed that IMPREG was activated via three compartments: immature neuronal-like malignant cells, myofibroblastic CAFs, or endothelial cells, forming niches devoid of effector T cells and enriched for TGFβ3, CXCL12, and IL-34-driven suppressive circuits. High IMPREG scores predicted ICB resistance in 14 cancer types, and was associated with increased sensitivity to EGFR inhibitors and anti-angiogenic therapies.

Contributed by Shishir Pant

ABSTRACT: Immune checkpoint blockade (ICB) has transformed oncology, yet most patients fail to respond, suffer from hyper-progressive disease, or face severe immune-related toxicities, underscoring the urgent need for biomarkers that identify non-responders. Here we show that tumors co-opt an immune-privileging regulon signature (IMPREG) mirroring transcriptional programs of immune-privileged organs - to enforce T-cell desertion and ICB resistance across solid tumor types. Single-cell and spatial transcriptomic analyses reveal that tumors activate IMPREG through three distinct cellular routes: malignant cells adopting immature neuronal states, cancer-associated fibroblasts assuming myofibroblast identities, or endothelial cells - each creating localized niches of immune suppression and antigen-presentation collapse. Across 4 discovery and 36 validation clinical datasets, IMPREG consistently predicts immunotherapy resistance in 14 distinct cancer types, functioning as an orthogonal marker independent of established biomarkers. Crucially, IMPREG-expressing tumors show enhanced sensitivity to EGFR inhibitors or anti-angiogenic therapies in specific tumor entities. These findings suggest IMPREG as a dual-utility predictive biomarker for personalized treatment stratification.

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC,

Author Info: 1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 3Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA. 4UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu. 5Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. xiaosongw@pitt.edu.

CD39+CD49a+CD103+ cytotoxic tissue-resident natural killer cells infiltrate and control solid epithelial tumor growth in mice

Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Human tissue-resident natural killer (NK) cells (trNK cells), broadly defined by markers of tissue residency, such as CD49a [integrin α1 (ITGA1)] and CD103 [integrin αE (ITGAE)], are increasingly recognized for their immunoregulatory role in host control of infection, malignancy, and autoimmunity. Although the importance of transforming growth factor-β in trNK cell differentiation has been demonstrated, the context in which the differentiation of CD49a+CD103+ trNK cells occurs can result in either an immunosuppressive phenotype (e.g., decidual NK cells) or a highly cytotoxic one (e.g., some tumor trNK subsets). To understand this dichotomy better, we used a multiomic approach to molecularly characterize these cells. We identified a cytotoxic trNK (ctrNK) cell population, characterized by the expression of CD39. These ctrNK cells exhibited superior cytolytic activity against tumor target cells, enhanced capacity to infiltrate into solid tumor microenvironments, and augmented ability to control solid tumor growth in vivo compared with conventionally activated peripheral NK cells. This heightened cytolytic and infiltrative functionality of ctrNK cells appeared to be conferred, in part, by the expression of CD103 and by avidity for tumor targets. Because adoptive immune cell therapy of solid tumor malignancies has been challenged by the inefficiency of ex vivo expanded immune cells to infiltrate immunosuppressive solid tumor microenvironments, our observations that ctrNK cells can be differentiated and expanded ex vivo present a potential platform for adoptive cell therapy of solid tumor malignancies.

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanfo

Author Info: 1Department of Otolaryngology-Head & Neck Surgery, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 2Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. 3Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA. 4Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. 5Pelotonia Institute for Immuno-Oncology, Ohio State University Comprehensive Cancer Center-the James, Columbus, OH 43210, USA. 6Department of Molecular Medicine and Therapeutics, College of Medicine, Ohio State University, Columbus, OH 43210, USA. 7Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. 8Division of Infectious Diseases, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. 9Section of Computational Biology, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. 10Siteman Cancer Center at WashU Medicine, St. Louis, MO 63110, USA.

Integrated Single-Cell Profiling Reveals Dichotomous NK Cell Populations Associated with Immunosuppression in Solid Tumors Featured  

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

Two recent papers phenotyped tumoral NK cell subsets. Lozada et al. detected a tissue-resident (TR) adaptive subset that was IFNG-driven, associated with better clinical outcomes and response to checkpoint blockade, while canonical NK cells expressed high TGFB1 and were suppressive. Horowitz, Mahammad, Ho Shin et al. also found that tissue-resident CD49a+CD103+ NK cells (trNK cells) can have suppressive or cytotoxic functions. A cytotoxic trNK population expressing CD39 had the highest cytolytic antitumor activity and could be differentiated and expanded ex vivo for adoptive transfer.

ABSTRACT: Natural killer (NK) cells represent key effectors of antitumor immunity, yet emerging evidence highlights populations with distinct roles in cancer. Despite such expanded diversity within the NK cell repertoire, we lack an understanding of how this heterogeneity impacts immune responses and downstream clinical outcomes. Using single-cell RNA-sequencing (scRNA-seq), we systematically profiled NK cells across cancer and uncovered a dichotomous phenotypic and functional landscape of tumor-infiltrating NK cells shaped by opposing intrinsic signaling programs that drive the expression of IFNG or TGFB1. These divergent programs are associated with distinct transcription factor circuits that integrate cues within the tumor microenvironment and skew NK cells towards pro-inflammatory or suppressive functions. We found that the capacity for NK cells to engage in either functional direction is intrinsically linked to their phenotypic identity. Canonical NK cells recruited from circulation predominantly directed suppressive TGFB1 signals towards effector CD8+ T cells in tumors. Of note, these subsets exhibited higher TGFB1 expression than intratumoral myeloid cells across tumor types. In contrast, a tissue-resident adaptive subset exhibited exclusively pro-inflammatory IFNG-driven profiles and was associated with prolonged survival in both primary and metastatic tumor settings. Moreover, these tissue-resident adaptive NK cells, but not other subsets, were linked to response to immune checkpoint blockade. Collectively, our study reveals a previously unrecognized regulatory axis in NK cells that shapes NK cell diversity and augments broader antitumor immune responses.

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United Stat

Author Info: 1University of Minnesota Minneapolis United States. 2Caris Life Sciences (United States) Irving, Texas United States. 3University of Minnesota Cancer Center Minneapolis United States. 4Caris Life Sciences (United States) Phoenix, AZ United States. 5University of Minnesota Minnesota, MN United States. 6University of Minnesota Minneapolis, Minnesota United States. 7Mayo Clinic Rochester, MN United States. 8University of Chicago Chicago, IL United States. 9Caris Life Sciences (United States) Los Angeles, CA United States. 10University of Minnesota Minneapolis, MN United States.

Time-of-day of first checkpoint inhibitor dose influences clinical outcomes and immune responses in hepatocellular carcinoma Spotlight 

Among a retrospective cohort of 84 HCC patients treated with ICB, those who received their first ICB dose in the morning (prior to 12 noon) had increased PFS (and a trend in OS) compared to those receiving a first dose in the afternoon. The timing of subsequent doses did not have a similar stratifying effect, and morning dosing did not raise the rate of irAEs. Comparing baseline and early on-treatment blood samples, Li et al. found that patients first receiving ICB in the morning had diminished induction of certain cytokines (IL-6, IL-1B, VEGF-A, and IL-21) and a greater expansion of cytotoxic CD8+ Tcm cells, compared to those receiving an afternoon dose.

Contributed by Alex Najibi

Among a retrospective cohort of 84 HCC patients treated with ICB, those who received their first ICB dose in the morning (prior to 12 noon) had increased PFS (and a trend in OS) compared to those receiving a first dose in the afternoon. The timing of subsequent doses did not have a similar stratifying effect, and morning dosing did not raise the rate of irAEs. Comparing baseline and early on-treatment blood samples, Li et al. found that patients first receiving ICB in the morning had diminished induction of certain cytokines (IL-6, IL-1B, VEGF-A, and IL-21) and a greater expansion of cytotoxic CD8+ Tcm cells, compared to those receiving an afternoon dose.

Contributed by Alex Najibi

BACKGROUND: Although immune checkpoint inhibitors (ICIs) have long half-lives, preclinical and retrospective clinical studies across multiple tumor types suggest that the time-of-day of ICI infusion may influence therapeutic efficacy by aligning initial drug exposure with circadian peaks in T-cell responsiveness. The immunological basis of this phenomenon and its clinical relevance in hepatocellular carcinoma (HCC) remains unknown. METHODS: We followed patients with advanced HCC receiving ICI therapy at Johns Hopkins from 2021 to 2025, classifying them into a morning (first treatment before 12:00 hours) or afternoon (first treatment after 12:00 hours) group. We assessed clinical outcomes and compared immunological responses from baseline to early-on-treatment by profiling peripheral blood mononuclear cells using cytometry by time-of-flight and plasma cytokines using a 39-plex Luminex assay. RESULTS: Our cohort included 84 patients, 39 of whom received their first infusion in the morning. There were no statistically significant differences in baseline demographic or clinical characteristics between patients initiating therapy in the morning versus afternoon. The morning group had superior progression-free survival (multivariable HR 0.50, 95% CI 0.30 to 0.84, p<0.01) and higher odds of treatment response (multivariable OR 3.26, 95% CI 1.08 to 10.90, p<0.05), with no significant increase in immune-related adverse events. The timing of subsequent infusions after the first dose had no impact on outcomes. Immunological responses diverged after the initial dose, with morning-treated patients showing reduced interleukin (IL)-6 levels (p<0.01) and greater expansion of cytotoxic central memory CD8+ T_cells (p=0.01) as well as cytotoxic effector and effector memory CD8+ T_cells (p=0.06). CONCLUSIONS: Morning first-dose infusion of ICIs in HCC was associated with improved clinical outcomes and distinct immune responses, including reduced IL-6 signaling and expansion of cytotoxic central memory CD8+ T cells. These findings suggest that the timing of the initial infusion can imprint an immunological program that shapes subsequent antitumor immunity, providing a mechanistic rationale for strategically scheduling ICI administration.

Author Info: (1) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (2) Sidney

Author Info: (1) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (2) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (3) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (4) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (5) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (6) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (7) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (8) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. (9) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (10) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (11) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (12) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (13) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (14) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (15) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (16) F Hoffmann-La Roche Ltd, Basel, Switzerland. (17) F Hoffmann-La Roche Ltd, Basel, Switzerland. Genentech Inc, South San Francisco, California, USA. (18) Genentech Inc, South San Francisco, California, USA. (19) Genentech Inc, South San Francisco, California, USA. (20) Genentech Inc, South San Francisco, California, USA. (21) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. Ludwig Institute for Cancer Research, Baltimore, Maryland, USA. (22) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (23) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (24) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (25) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (26) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (27) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. (28) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA mark.yarchoan@jhmi.edu mnakaza2@jhmi.edu. (29) Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA mark.yarchoan@jhmi.edu mnakaza2@jhmi.edu.

Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression Spotlight 

Using single-cell and spatial transcriptomics in human and rat models, Bui et al. mapped immune remodeling of normal breast, pre-malignant (DCIS) , and invasive (IBC) breast cancer and identified a proliferative FOXP3int MKI67hi cycling Treg (cycTreg) subset. CycTregs emerged at the DCIS-IBC junction, expanded in IBC, and predicted CD8+ infiltration, TCR diversity, disease-specific survival, and DCIS recurrence. CycTreg abundance correlated with CLEC10A+ cDC2s, high HLA class II, and IL-33-producing CAFs. OX40 agonism plus anti-PD-L1 or IL-33 blockade reduced cycTreg, remodeled CAF states, and restored antitumor immunosurveillance.

Contributed by Shishir Pant

Using single-cell and spatial transcriptomics in human and rat models, Bui et al. mapped immune remodeling of normal breast, pre-malignant (DCIS) , and invasive (IBC) breast cancer and identified a proliferative FOXP3int MKI67hi cycling Treg (cycTreg) subset. CycTregs emerged at the DCIS-IBC junction, expanded in IBC, and predicted CD8+ infiltration, TCR diversity, disease-specific survival, and DCIS recurrence. CycTreg abundance correlated with CLEC10A+ cDC2s, high HLA class II, and IL-33-producing CAFs. OX40 agonism plus anti-PD-L1 or IL-33 blockade reduced cycTreg, remodeled CAF states, and restored antitumor immunosurveillance.

Contributed by Shishir Pant

ABSTRACT: Immune escape during the ductal carcinoma in situ (DCIS)-to-invasive breast cancer (IBC) transition shapes tumor evolution. Through transcriptomic mapping of the immune landscapes of normal breast, DCIS, and IBC from large patient cohorts, we identified T and myeloid cells as the primary distinguishing features between DCIS and IBC. We discovered cycling regulatory T cells (cycTreg) as an orchestrator of immunosuppression in IBC. cycTreg frequency predicts cytotoxic CD8(+), TCR diversity, disease-specific survival in IBC, and recurrence in DCIS. In a rat model of breast cancer, we demonstrated that cycTreg act as precursors to mature Treg and are inducible by tumor-localized type 2 dendritic cells. Profiling of tumors subjected to OX40 and PD-L1 therapies revealed an IL-33-mediated fibroblast-cycTreg signaling loop, the disruption of which enhances intratumoral antigen-experienced CD8(+) effectors and systemic immunosurveillance. Our study defines cycTreg as critical inducers of immune escape and promising immuno-oncology targets in breast cancer.

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of

Author Info: (1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (2) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (3) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (4) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (5) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (6) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (7) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (8) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (9) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (10) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (11) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (12) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. (13) Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27705, USA. (14) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (15) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (16) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA. (17) Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA. (18) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (19) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (20) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (21) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (22) Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA. (23) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (24) Department of Surgery, Duke University School of Medicine, Durham, NC 27708, USA. (25) Department of Surgery, Washington University School of Medicine, St. Louis, MO 63108, USA. (26) Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea. (27) Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA. (28) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (29) UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA. (30) Sutter Institute for Medical Research, Roseville, CA 95661, USA. (31) Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. (32) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. (33) Department of Surgery, Duke University School of Medicine, Durham, NC 27708, USA. (34) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. (35) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: kornelia_polyak@dfci.harvard.edu.

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