Fig 2: DT alters the inflammatory profile of tumor-bearing lungs. Infiltrating immune cells from healthy lungs and from lung tumors derived from FVB TetO-EGFRL858R; CCSP-rtTA mice treated with PBS (vehicle), CRM197 (CRM) or diphtheria toxin (DT) for 11 days were analyzed by flow cytometry. (A) Representative contour plots demonstrating the gating strategy used to identify distinct myeloid populations and their production of TNFa. (B,C) Percentage of CD11b+ Ly6C+ Ly6G+ neutrophils (B) and CD11b- CD11c+ SiglecF+ alveolar macrophages (C). (D) Percentage of SiglecF+ cells that produce TNFa. Results are shown as mean±s.e.m. with P-values calculated using one-way ANOVA (B-D). n.s., not significant.

Fig 3: Phagocytes are crucial to antitumour effect of ROCK blockade. a–c CT26.CL25 tumour-bearing BALB/c mice (a) and B16F10-Ova tumour-bearing C57BL/6 mice (b) were injected (i.v.) with Y27632 (10 mg kg-1) or vehicle (PBS). Tumour dimensions were measured at the indicated times using calipers and tumour volume was calculated (mm3). Data are presented as means ± s.e.m. (left panels) and tumour growth curves of individual mice (right panels) (CT26.CL25 model, n = 17; B16F10-Ova model, n = 17–19). c Tumour weights in individual mice were analysed at the end of experiments. Each dot represents tumour weight of individual mice. Bars indicate mean values. d–g CT26.CL25-mCherry tumour-bearing BALB/c mice were injected (i.v.) with Y27632 (10 mg kg-1) together with clodronate liposomes or PBS liposomes as described in Supplementary Fig. 6c. d Tumour volume was measured at the indicated time. Data are presented as means ± s.e.m. (left panel) and tumour growth curves of individual mice (right panels) (n = 9). e, f For the PBS liposomes-treated group, F4/80+ macrophages or CD11c+ DCs were isolated from tumours, and the percentage of cells harbouring mCherry+ tumour cells was determined by flow cytometry. e Representative flow cytometry plots. The grey peaks represent mCherry levels in macrophages and DCs from CT26.CL25 tumour-bearing mice (mCherry-negative tumour). f Data are presented as means ± s.d. (n = 3). g At 22 days after tumour inoculation, single-cell suspensions from tumour-draining LNs were stimulated with ß-gal-derived peptide for 48 h, and secreted IFN-? was measured by ELISA. Data are presented as means ± s.d. (n = 4). h At 22 days after tumour inoculation, CD8+ and CD8- cells were isolated from tumour-draining LNs and stimulated with ß-gal-derived peptide for 48 h, and secreted IFN-? was measured by ELISA. Data are presented as means ± s.d. (n = 4). *P < 0.05, **P < 0.01, ***P < 0.001; significance determined by unpaired Student’s t test (a, f), Mann–Whitney test (b, c) or one-way ANOVA with Tukey’s post hoc test (d, g, h)

Fig 4: Hydrogel-enabled local administration of KYNase and Dox inflames the TME and suppresses established 4T1 TNBC.a The experimental design. 4T1 cancer cells were implanted subcutaneously on day 0. Mice were treated with empty hydrogel, Dox-loaded hydrogel, KYNase-loaded hydrogel, free KYNase and Dox, free KYNase and Dox administered as 3 doses, or KYNase- and Dox-loaded combination hydrogel on day 10 when tumor volumes reached 100 mm3. b Average tumor growth over the course of the study. n = 5 mice for PBS and n = 7 mice for the other groups. c Survival curves for all treatment groups. d–k Following 4T1 tumor inoculation on day 0, different treatments were administered on day 10 when tumor volumes reached 100 mm3. Tumors were analyzed 4 days after treatment. n = 3 mice for PBS and empty hydrogel groups, n = 5 mice for the combination hydrogel group, and n = 4 mice for the other groups. d, e, Representative FACS plots (d) and quantification (e) of CD8+ and CD4+ TILs. f, g Quantification of IFN?+, TNFa+, IFN?+TNFa+, and IL2+ CD8+ (f) and IFN?+CD4+ (g) TILs after ex vivo stimulation. h Quantification of CD39+CD8+ TILs. i, j Percentage of CD86+ DCs (i) pre-gated on CD45+CD11c+ populations, and M1-like macrophages (j) pre-gated on CD45+CD11b+F4/80+ populations in tumor tissues. k Percentage of PD-L1+ and calreticulin+ tumor cells pre-gated on CD45- populations. Throughout, data are presented as mean ± s.e.m., and statistical significance was determined by two-way ANOVA with Turkey’s post hoc test (b), one-way ANOVA with Turkey’s post hoc test (e–k) or log-rank (Mantel-Cox) test (c). Source data are provided as a Source Data file.

Fig 5: Immune tumor microenvironment in PN- and MES-GBM tumors.(a) MES gene signature was used to classify patients into the MES-high, MES-intermediate and MES-low groups. (b) The relative percentage of each cell type across different groups. (c) TCGA dataset reveals gene expression patterns associated with proneural, classical, and mesenchymal subtypes of GBM. Statistical analysis by one-way ANOVA test. (d) Pearson correlation between cell-type-specific genes in the GBM-TCGA dataset, with blue representing a negative correlation and red indicating a positive correlation. (e) Spatial distribution of cell-type-specific scores/genes defined by scRNA-seq. (f) Association of macrophage/dendritic cells with T cell fractions, which were estimated by a cellular deconvolution algorithm. The correlation was conducted using linear regression analysis in TCGA datasets. (g) Flow cytometry analysis of CD3+ T cells, CD11b+F4/80+ TAMs and CD11c+MHCII+ DCs in GBM mouse (n=18). Left, gating strategy. Right, a linear relationship was evaluated using Pearson correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)