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Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus <t>anti-CD8a</t> antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.
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Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus <t>anti-CD8a</t> antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.
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Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus <t>anti-CD8a</t> antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.
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Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus <t>anti-CD8a</t> antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.
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Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus anti-CD8a antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.

Journal: Cancer Discovery

Article Title: Senescent-Like Neutrophils Shape Angiogenic Immunosuppressive Niches in Colorectal Cancer Liver Metastasis

doi: 10.1158/2159-8290.CD-25-0820

Figure Lengend Snippet: Proangiogenic and immunosuppression phenotype of TANs in the CRLM microenvironment. A, Heatmap showing signature scores for angiogenesis, myeloid cell recruitment, neutrophil cytotoxicity, MDSC, and NET across each TAN and PMN subset. B, Comparison of angiogenesis, myeloid cell recruitment, MDSC, and NET signature scores among TAN and PMN subsets. C, Dot plot illustrating expression levels of major cytokines and chemokines in each TAN and PMN subset. D, Representative IHC images of CD31 staining in CRLM tissues. Scale bars, 50 µm. E, Correlation analysis between CD31 and TAN1 density using Spearman’s rank correlation ( n = 102). F, Representative images and quantification of tube formation in HUVECs when cocultured with conditioned medium from human LGALS3 − neutrophils or LGALS3 + neutrophils ( n = 6). G, UMAP plot showing six monocyte/macrophage subsets across colorectal cancer, adjacent liver tissues, CRLM, and PB samples. H, Shell-like plot depicting ligand–receptor interaction strength between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. I, Selected ligand–receptor interactions between TAN1_LGASL3 and neutrophil/monocyte/macrophage subset in CRLM. J, Representative mIHC images of CD68, CCL3L1, CD66b, CCR1, and BHLHE40 in CRLM. Scale bars, 50 µm. K, Representative images of macrophage migration assays and effects of conditioned medium from human LGALS3 + neutrophils on migration of macrophages (induced from THP-1 cells) in the presence of CCR1 inhibitor (BX471) or anti-CCL3 antibody ( n = 6). L, Comparison of immunosuppressive macrophage signature scores between colorectal cancer and CRLM. M, Schematic of the CRLM mouse model. C57BL/6 mice received intrasplenic injections of mouse colorectal cancer cells (MC38). Mice were subsequently treated with IgG control (IgG; n = 6), anti-Ly6G antibody (anti-Ly6G; n = 6), or anti-Ly6G plus anti-CD8a antibodies (anti-Ly6G + anti-CD8a; n = 6). Livers were harvested on day 14. N, Representative images of liver metastases from IgG, anti-Ly6G, and anti-Ly6G + anti-CD8a groups. O, Comparison of liver weights among the three groups. P values were calculated by the Wilcoxon rank-sum test ( B and L ) and unpaired Student t test ( F , K , and O ). *, P < 0.05; **, P < 0.01; ***, P < 0.001. CRC, colorectal cancer; Ctrl, control.

Article Snippet: For CD8 + T-cell depletion, 200 μg anti-mouse CD8a (Bio X Cell, BE0061, RRID: AB_1125541) was injected intraperitoneally twice a week.

Techniques: Comparison, Expressing, Staining, Analysis, Migration, Control