Cancer cells impair monocyte-mediated T cell stimulation to evade immunity

Abstract

The tumour microenvironment is programmed by cancer cells and substantially influences anti-tumour immune responses^1,2. Within the tumour microenvironment, CD8⁺ T cells undergo full effector differentiation and acquire cytotoxic anti-tumour functions in specialized niches^3-7. Although interactions with type 1 conventional dendritic cells have been implicated in this process^3-5,8-10, the underlying cellular players and molecular mechanisms remain incompletely understood. Here we show that inflammatory monocytes can adopt a pivotal role in intratumoral T cell stimulation. These cells express Cxcl9, Cxcl10 and Il15, but in contrast to type 1 conventional dendritic cells, which cross-present antigens, inflammatory monocytes obtain and present peptide-major histocompatibility complex class I complexes from tumour cells through 'cross-dressing'. Hyperactivation of MAPK signalling in cancer cells hampers this process by coordinately blunting the production of type I interferon (IFN-I) cytokines and inducing the secretion of prostaglandin E₂ (PGE₂), which impairs the inflammatory monocyte state and intratumoral T cell stimulation. Enhancing IFN-I cytokine production and blocking PGE₂ secretion restores this process and re-sensitizes tumours to T cell-mediated immunity. Together, our work uncovers a central role of inflammatory monocytes in intratumoral T cell stimulation, elucidates how oncogenic signalling disrupts T cell responses through counter-regulation of PGE₂ and IFN-I, and proposes rational combination therapies to enhance immunotherapies.

Fig. 1. Tumour-infiltrating T cells are restimulated in permissive TMEs.

a, Schematic of subcutaneous injection of YUMM1.7^OVA N^TT cells or R^TT cells in Rag2^–/– mice and OT-1^Luc ACT through intravenous (i.v.) injection. b, Left, ACT responses of N^TT and R^TT tumours (n = 5 mice per group). Right, representative bioluminescence imaging (BLI) pictures of T cells 96 h after ACT; key indicates radiance (photons per second that leave a square centimeter of tissue and radiate into a solid angle of one steradian). c, Uniform manifold approximation and projection (UMAP) maps of scRNA-seq of CD45⁺ cells in N^TT and R^TT tumours 72 h after ACT (n = 4 tumours pooled per group). d, Relative cell frequencies from scRNA-seq. e, Representative multiparameter IF images of YUMM1.7^OVA N^TT and R^TT tumours 48 h after ACT (n  = 3 tumours per group). Scale bar, 400 µm (zoom-ins, 50 µm and 10 µm). f, Relative T cell frequency and distance to the next immune cell in N^TT tumours (n = 3 tumours per group). g, Representative IF image of Nur77–GFP OT-1 cells in YUMM1.7^OVA N^TT and R^TT tumours 48 h after ACT (n = 3 tumours per group). Scale bar, 10 µm. Arrows indicate Nur77⁺ OT-1 cells. h, Representative IF image of YUMM1.7^OVA N^TT and R^TT tumours 72 h after ACT. Dashed lines depict the tumour border (n = 2 tumours per group). Scale bar, 500 µm. i, Left, quantification of T cells by BLI over time after intratumoral (i.t.) ACT (mean ± s.e.m., n = 4 mice per group). Two-way analysis of variance (ANOVA) with Sidak’s multiple comparisons test. Right, representative BLI image 96 h after ACT (key as in b). j, T cell states by flow cytometry 120 h after i.t. ACT (mean ± s.e.m., n = 5 N^TT, n = 4 R^TT tumours). k, Effector memory T cell signature on scRNA-seq of T cells (n = 14 N^TT, n = 18 R^TT pooled tumours). l, Left, representative BLI images of T cells in N^TT and contralateral (CL) R^TT tumours after i.t. ACT (key as in b). Right, growth curves (mean ± s.e.m., n = 6 mice per group). Arrows in b and l indicate day of ACT. Source Data

Fig. 2. Inflammatory monocytes restimulate T cells within the TME.

a, Left, ACT response of YUMM1.7^OVA N^TT tumours in Rag2^–/– mice (n = 4 mice) and Batf3^–/–Rag2^–/– (n = 5 mice). Right, representative BLI image of T cells 96 h after ACT (key as in Fig. 1b). b, Gene expression in individual clusters from scRNA-seq from Fig. 1c. c, Scoring of an ISG signature on our scRNA-seq data from Fig. 1c. Mod., module. d, Regulon specificity score of monocytes calculated using SCENIC in N^TT tumours. e, Left, representative histograms depicting CFSE intensity. Right, quantification of T cell proliferation after 72 h of co-culture of naive CFSE-labelled T cells and inflammatory (Ly6A⁺) or non-inflammatory monocytes (Ly6A^–) isolated from N^TT tumours in Rag2^–/– mice (n = 4 tumours). Two-tailed unpaired Student’s t-test. f, Left, schematic of injection of YUMM1.7^OVA N^TT CTRL or B2m KO cells in BALB/c mice. Right, representative histograms depicting H2-K^b levels. g, Left, schematic of injection of N^TT CTRL or N^TT B2m KO tumours in Rag2^–/– mice and Batf3^–l–Rag2^–/– mice. Middle, quantification of T cell infiltration by BLI. Right, representative BLI image of T cell infiltration 96 h after ACT (n = 5 N^TT in Rag2^–/– and n = 6 mice for other groups) (key as in Fig. 1b). One-way ANOVA with Tukey’s multiple comparisons test. h, Left, UMAP of human inflammatory macrophages from a melanoma scRNA-seq myeloid dataset. Right, enrichment score (ES) values of the inflammatory monocyte gene signature for each cell cluster. i, Top, representative field of view (FOV) of human metastatic melanoma (n = 2 of 72 FOVs) analysed by CosMx spatial transcriptomics profiling. Bottom, Pearson’s correlation values between cell types across FOVs (n = 72) were determined and displayed as a heatmap (n = 34 melanoma samples). The white arrow depicts activated CD8⁺ T cells, the black arrow depicts CXCL9⁺CXCL10⁺C1QC⁺ macrophages, and the red arrow CXCL9⁺CXCL10⁺ macrophages and DCs. Mac, macrophage. Bar graphs depict the mean ± s.e.m. Source Data

Fig. 3. Cancer cells produce PGE₂ and downregulate IFN-I responses to confer immunotherapy resistance.

a, Pathway enrichment analysis of differential gene expression in cancer cells isolated from YUMM1.7^OVA N^TT tumours and R^TT tumours (n = 3 tumours per group; Supplementary Table 3). Adjusted P values were computed using the Benjamini–Hochberg correction. b, PGE₂ ELISA of YUMM1.7^OVA tumours in Rag2^–/– mice (n = 10 N^TT, n = 11 R^TT CTRL, n = 7 R^TT Ptgs1/2 KO over 2 independent experiments). c, Top, representative BLI image of T cells 96 h after ACT (key as in Fig. 1b). Bottom, BLI quantification (n = 4 mice N^TT, n = 6 mice R^TT CTRL and R^TT Ptgs1/2 KO). d, Left, response of YUMM1.7^OVA tumours in Rag2^–/– mice to ACT (n = 7 mice per group). Right, YUMM3.3 in C57BL/6 mice (n = 5 mice per group). e, IFNβ ELISA of supernatants from YUMM1.7^OVA N^TT and R^TT cells (n = 5 replicates per group over 2 independent experiments). f, Top, representative BLI images of T cells in YUMM1.7^OVA R^TT CTRL and R^TT IRF3/7 tumours in Rag2^–l– mice 96 h after ACT (key as in Fig. 1b). Bottom, BLI quantification (n = 6 mice per group). g, Left, response of YUMM1.7^OVA tumours in Rag2^–/– mice to ACT (n = 4 mice R^TT CTRL, n = 5 mice R^TT IRF3/7). Right, YUMM3.3 in C57BL/6 mice (n = 6 mice per group). h, Response to ACT of YUMM1.7^OVA R^TT Ptgs2 KO (left) and R^TT IRF3/7 tumours (right) in Rag2^–/– (n = 4 mice per group) and Batf3^–/–Rag2^–/– mice (n = 5 mice per group). i, PGE₂ and IFNβ ELISAs of N^TT and R^TT A375 human melanoma (n = 2 replicates per group for PGE₂, n = 3 tumours per group for IFNβ). Arrows in d, g and h indicate day of ACT. Bar graphs depict the mean ± s.e.m. Statistical analysis was performed with a two-tailed unpaired Student’s t-test (e,f,i) or one-way ANOVA with Tukey’s multiple comparisons test (b,c). Source Data

Fig. 4. PGE₂ and IFN-I responses determine myeloid cell abundance and their functional inflammatory state in the TME.

a, UMAP of scRNA-seq of CD45⁺ cells in YUMM1.7^OVA R^TT Ptgs1/2 KO and R^TT IRF3/7 tumours 72 h after ACT (n = 3 tumours pooled per group). See Fig. 1c for cell cluster annotation. b, Scoring of the TME-COX and TME-IRF3/7 signatures (Supplementary Table 4) in myeloid fractions of responder (n = 6) and non-responder (n = 7) patients before TIL infusion. c, Scoring of an ISG signature in the scRNA-seq from a. d, Flow cytometry quantification of myeloid populations normalized to the CD45⁺ fraction from YUMM1.7^OVA N^TT tumours in Rag2^–/– mice treated with anti-IFNAR1 or anti-IFNγ (n = 5 tumours per group, except n = 4 in anti-IFNAR1 + ACT and ant-IFNγ + ACT). e, RNA velocity of the MoMac compartment from R^TT Ptgs1/2 KO tumours. f, Representative contour plots of Ly6C⁺ monocytes depicting Ly6A expression 72 h after i.t. transfer into N^TT and R^TT tumours in Rag2^–/– mice (n = 5 tumours per group). g, Left, BLI quantification of T cells 96 h after ACT of YUMM1.7^OVA R^TT cells into Cd11c^cre-Ptger2^–/–Ptger4^fl/fl mice (EP2/EP4 KO) or Cd11c^cre (CTRL) mice (n = 7 mice per CTRL group and n = 8 mice per EP2/EP4 KO group); two-tailed Mann–Whitney U-test. Right, representative BLI image (key as in Fig. 1b). h, Representative contour plots of Ly6C⁺ monocytes depicting expression of Ly6A 48 h after treatment with CM from cancer cells with or without a COX1/2i (indomethacin) and with or without anti-ΙFNAR1 or isotype (n = 3 biological replicates). i, Heatmap of scaled ISG expression in BMDCs exposed to CM from N^TT or R^TT cells and with or without a COX1/2i in the presence of anti-ΙFNAR1 or isotype (n = 4 technical replicates) measured by RT–qPCR. j, Heatmap of scaled ISG expression in BMDCs treated with IFNβ and PGE₂ measured by RT–qPCR (n = 4 technical replicates). Bar graphs depict the mean ± s.e.m. Source Data

Fig. 5. Pharmacological modulation of PGE₂ and IFN-I responses reinstates an immune-permissive TME and immunotherapy response.

a, Forest plot of pooled odds ratios and 95% CIs across clinical studies for overall response rates in patients receiving ICB with or without NSAID co-medication (n = 722 patients over 8 independent cohorts; Extended Data Fig. 9a and Supplementary Table 5). Statistical analysis was performed with a random effects model and data are presented as the mean ± 95% CI. b, UMAP of scRNA-seq of CD45⁺ cells of R^TT CTRL and COX2i-treated YUMM1.7^OVA R^TT tumours 72 h after ACT (n = 3 tumours pooled per condition). See Fig. 1c for cell cluster annotation. c, Relative frequency of cell types across conditions as assessed by scRNA-seq. d, Left, treatment schedule of YUMM1.7^OVA R^TT tumours in Rag2^–/– mice with celecoxib (COX2i) in combination with FLT3L or 5-AZA. Right, BLI quantification. Top, representative BLI images 72 h after ACT for vehicle (n = 8 mice), COX2i (n = 9 mice), COX2i + 5-AZA (n = 6 mice) and COX2i + FLT3L (n = 8 mice) groups (key as in Fig. 1b). Bar graphs depict the mean ± s.e.m. One-way ANOVA with Tukey’s multiple comparisons test. e, Left, Survival of Rag2^–/– mice bearing YUMM1.7^OVA R^TT tumours treated with ACT and vehicle (n = 8 mice), COX2i (n = 9 mice), COX2i + 5-AZA (n = 7 mice) or COX2i + FLT3L (n = 8 mice). Right, survival of C57BL/6 mice bearing YUMM3.3 R^TT tumours treated with an anti-PD1 and anti-CTLA4 with vehicle (n = 6 mice), COX2i (n = 8 mice), COX2i + FLT3L (n = 9 mice) or COX2i + 5-AZA (n = 9 mice). Log-rank Mantel–Cox test. Source Data

Extended Data Fig. 1. Characterization of immune permissive versus suppressive TMEs in mouse melanoma models.

a, Representative images of bioluminescent live imaging (BLI) of T cell infiltration after adoptive CD8⁺ OT-1^Luc T cell transfer (ACT) in YUMM1.7^OVA N^TT and R^TT tumors. b, Scheme outlining the in vitro killing of N^TT and R^TT cells with activated CD8⁺ OT-1 T cells (1:1 ratio) (left), histograms (flow cytometry) displaying MHCI levels of N^TT and R^TT cells (middle) and normalized ratio of 50:50 N^TT/R^TT cells mixed cultures in an in vitro killing assay with CD8⁺ OT-1 T cells. Normalization was performed with N^TT and R^TT percentages from the same 50:50 mixed cultures without CD8⁺ OT-1 T cells to account for proliferation differences (n = 3 biological replicates) (right). Two-tailed unpaired Student’s t-test. c, UMAP of scRNA-seq of CD45⁺ cells of YUMM1.7^OVA N^TT and R^TT tumors in Rag2^−/− before ACT (n = 3 tumors pooled/group). d, Relative frequency of cell types across conditions in YUMM1.7^OVA tumors post-ACT (n = 4 tumors pooled/group). e, Expression of myeloid cell markers in each cluster (scRNA-seq). f, Heatmap of scaled gene expression (scRNA-seq) across cell clusters between N^TT and R^TT YUMM1.7^OVA tumors post-ACT. g, Response to immune checkpoint blockade (ICB, anti-PD1/CTLA-4) of YUMM3.3 N^TT and R^TT tumors (n = 6 N^TT, n = 7 N^TT + ICB, n = 8 R^TT, n = 9 R^TT + ICB). Two-way ANOVA with Tukey’s multiple comparison. h, UMAP of scRNA-seq of CD45⁺ cells of YUMM3.3 N^TT and R^TT (n = 1 tumor/group). i, Relative frequency of cell types across conditions. j, Heatmap of scaled gene expression (scRNA-seq) across cell clusters between YUMM3.3 N^TT and R^TT. Bar graphs and growth curves depict the mean ± s.e.m. ns = not significant. Source Data

Extended Data Fig. 2. Permissive TMEs act as local reservoirs for T cell expansion.

a, Representative immunofluorescence (IF) of YUMM1.7^OVA N^TT and R^TT tumors 48 h post-ACT(n = 3 tumors/group). Scale bars depicted in image. b, Quantification of immune populations based on IF staining (n = 3 tumors/group). c, Quantification of relative T cell frequency and distance to the tumor periphery in N^TT and R^TT tumors. Percentages of T cells found in the tumor border or tumor center are depicted (n = 3 tumors per condition). d, Quantification of Nur77⁺ OT-1 cells in YUMM1.7^OVA N^TT and R^TT tumors 48 h post-ACT (n = 6 tumors for N^TT, n = 5 for R^TT). e, Average distances between Nur77⁺ OT-1 cells and cDC1s or monocytes based on IF stainings (n = 6 tumors for N^TT, n = 5 for R^TT). f, Quantification of CD8⁺ OT-1^Luc T cell infiltration by BLI post-ACT in YUMM1.7^OVA N^TT and R^TT in Rag2^−/− mice treated with FTY720 (n = 5 mice/group). g, Quantification of T cell proliferation by CFSE dilution via flow cytometry 72 h post intratumoral (i.tu.) T cell injection into N^TT and R^TT tumors in Rag2^−/− (n = 5 mice/group). h, Relative frequencies of T cell phenotypes (flow cytometry) after activation in vitro prior to ACT (n = 1, experiment repeated 3 times). i, Gating strategy for the phenotypic characterization of T cells. j, Relative frequencies of CD8⁺ T cell populations based on defined T cell signatures of i.tu. injected T cells isolated and subjected to scRNA-seq 5 days post-transfer (n = 14 N^TT, n = 18 R^TT pooled tumors). k, Projection of a progenitor exhausted T cell signature score and Cxcr6 gene expression on scRNA-seq from intratumoral T cells. l, Scheme outlining the contralateral (CL) injection of tumors in opposite flanks of the same mouse followed by intravenous (i.v) or i.tu. ACT in one of the tumors. m, Growth curves depicting the response to i.v ACT of primary or CL tumor in the same mouse (n = 4 mice N^TT/N^TT, n = 3 mice R^TT/R^TT and n = 6 mice N^TT/R^TT). n, Quantification of T cell infiltration by BLI in mice harboring CL N^TT/N^TT tumors (n = 6 mice), R^TT/R^TT tumors (n = 3 mice) or an N^TT and R^TT tumor (n = 10 mice) after i.v ACT. o, Representative BLI images from Extended Data Fig. 2m, n. p, Flow cytometry characterization of the TME of tumors in the CL setting (n = 3 mice N^TT/N^TT, n = 3 mice R^TT/R^TT, n = 5 mice N^TT/R^TT tumors). q, Quantification of T cell infiltration by BLI post-ACT in N^TT and R^TT in Rag2^−/− mice treated with FTY720 (n = 7 mice/group). r, Response to ACT of mice bearing CL tumors (N^TT/R^TT) and treated with FTY720 or untreated (CTRL), (n = 7 mice/group). s, Response to ACT of Rag2^−/− mice bearing CL tumors (N^TT/N^TT) with one of the N^TT tumors injected i.tu (n = 6 mice). t, Response to ACT of Rag2^−/− mice bearing CL tumors (N^TT/R^TT) with R^TT tumors injected i.tu (n = 6 mice). u, Representative BLI images of T cell infiltration in mice harboring N^TT/N^TT, R^TT/ R^TT or R^TT/ N^TT tumors, with one of the tumors receiving i.tu. T cell transfer (n = 6 mice). Bar graphs and growth curves depict the mean ± s.e.m. Error bars in s and t not visible due to synchronous tumor regression. Statistical analysis was performed with a two-tailed unpaired student’s t-test in b, c, d, and g. A one-way ANOVA with Tukey’s multiple comparisons test in f, n, p and q. Two-way ANOVA with Tukey’s multiple comparisons test in m and r on day 17 pi. ns = not significant. Arrow indicates day of ACT. Source Data

Extended Data Fig. 3. Inflammatory monocytes cross-dress with cancer-derived pMHCI complexes.

a, Scheme outlining the dendritic cell (DC) vaccination of YUMM1.7^OVA R^TT tumors with bone marrow-derived cDC1s (BM-DCs) injected i.tu. and treated with ACT. b, Left, representative BLI images and right, quantification of T cell infiltration by BLI (n = 6 mice CTRL and n = 7 mice for DC vaccination). c, Response to ACT of R^TT tumors vaccinated with BM-DCs followed by ACT. Black arrow indicates the day of ACT and gray arrows the days of cDC1 vaccination (n = 6 mice CTRL and n = 7 mice for DC vaccination). d, Representative IF staining of YUMM1.7^OVA N^TT and R^TT tumors in Batf3^{−/ −} Rag2^−/− mice, 48 h post ACT, scale bar in N^TT = 500 µm, zoom-ins=20 µm and 10 µm, scale bar in R^TT = 1000 µm, (n = 3 tumors/group). e, Quantification of relative T cell frequency and distance to the tumor periphery in N^TT and R^TT tumors injected in Rag2^−/− Batf3^−/− mice. Percentages of T cells found in the tumor border or tumor center are depicted (n = 3 tumors/group). f, Quantification of relative T cell frequency and distance to the next immune cell in N^TT tumors in Rag2^−/− Batf3^−/− mice (n = 3 tumors). g, Scheme outlining the depletion of dendritic cells with diphtheria toxin (DT) in N^TT tumors in Rag2^−/− mice reconstituted with bone marrow from Rag2^−/− Zbtb46-DTR mice. DT was administered every 3 days starting on the day of tumor injection for the duration of the experiment. h, Contour plots depicting the frequency of dendritic cells (left) and flow quantification (flow cytometry) (n = 3 tumors/group). i, Representative BLI images (left) and quantification of T cell infiltration (n = 5 mice for untreated, n = 6 DT treated) (right). j, Scoring of a monocyte derived dendritic cell (MonoDC) signature in the YUMM1.7^OVA scRNA-seq. k, Scoring of the Bosteels et al. ISG signature (Supplementary Table 2) in the YUMM1.7^OVA scRNA-seq. l, Quantification of Nur77⁺ or Nur77^- OT-1 T cells interacting with Ly6c⁺ monocytes based on IF staining of N^TT tumors 48 h post-ACT (n = 6 tumors). m, Median fluorescence intensity (MFI) quantification of SIINFEKL-H2K^b staining (n = 6 mice/group) (left) and histograms (right). n, MFI quantification of N^TT-derived H2-K^b on BALB/c pMHC-I cross-dressed monocytes (n = 6 mice/group) in N^TT and N^TT B2m KO. o, Scheme of the process of cross-dressing and in vitro T cell activation (left) and quantification of OT-1 T cell proliferation after 72 h of co-culture with monocytes FACS-sorted from YUMM1.7^OVA N^TT tumors in BALB/c mice (n = 3 technical replicates). Bar graphs depict the mean ± s.e.m. Statistical analysis was performed with a two-tailed unpaired student’s t-test in b, e, h, i, l, m, n and o. ns = not significant. Source Data

Extended Data Fig. 4. Inflammatory monocytes correspond to ISG⁺ and CXCL9⁺ macrophages in human data-sets.

a, Inflammatory monocyte and Monocyte 1 gene signature projection on a UMAP, and enrichment scores (ES) calculated with Gene Set Variance Analysis (GSVA) for different myeloid populations in a human melanoma scRNA-seq myeloid data-set (MEL) (Cheng et al.). b, Inflammatory monocyte and Monocyte 1 gene signature projection on a UMAP, and ES calculated with GSVA for different myeloid populations, in human non-small cell lung cancer (NSCLC) scRNA-seq myeloid data-set (Cheng et al.). c, Inflammatory monocyte and Monocyte 1 gene signature projection on a UMAP, and ES calculated with GSVA for different myeloid populations, in human melanoma scRNA-seq myeloid data-set (Barras et al.). See also Supplementary Table 2.

Extended Data Fig. 5. Oncogenic MAPK signaling counter-regulates the production of PGE₂ and IFN-I.

a, Targeted metabolomic analysis of eicosanoids in YUMM1.7^OVA N^TT and R^TT tumors isolated at day 10 post-injection from Rag2^−/− mice (n = 3 tumors/group). b, Tumor growth of non-ACT R^TT CTRL and R^TT Ptgs1/2 KO (n = 7 mice/group) in Rag2^−/− mice. c, Scheme outlining the injection of YUMM1.7^OVA R^TT Ptgs2 KO into Rag2^−/− mice followed by ACT. d, Quantification of PGE₂ by ELISA from YUMM1.7^OVA R^TT Ptgs2 KO tumors isolated at day 10 post-injection (n = 4 for R^TT CTRL and n = 6 for R^TT Ptgs2 KO, over 2 independent experiments). e, Survival of Rag2^−/− mice harboringYUMM1.7^OVA R^TT CTRL or R^TT Ptgs2 KO tumors upon ACT treatment (n = 5 mice/group). f, Quantification of PGE₂ by ELISA from YUMM3.3 N^TT and R^TT tumors (n = 2 biological replicates). g, Tumor growth of YUMM3.3 R^TT CTRL and R^TT Ptgs2 KO (n = 5 mice/group) in Rag2^−/− mice. h, Quantification of IFN-β by ELISA from YUMM1.7^OVA N^TT, R^TT and R^TT IRF3/7 tumors grown in Rag2^−/− mice normalized to tumor weight (n = 4 tumors/group, pooled from 2 independent experiments). i, Upstream regulator analysis (Ingenuity) of differentially expressed genes in cancer cells sorted from R^TT vs. N^TT YUMM1.7^OVA tumors grown in Rag2^−/− mice. Benjamini-Hochberg correction for multiple testing. j, Tumor growth of non-ACT treated YUMM1.7^OVA R^TT CTRL (n = 5 mice) and R^TT IRF3/7 (n = 4 mice) in Rag2^−/− mice. k, Quantification of IFN-β by ELISA from YUMM3.3 N^TT and R^TT tumors grown in C57BL/6 mice normalized to tumor weight (n = 3 tumors/group). l, Heatmap of scaled ISG expression in YUMM1.7^OVA R^TT upon treatment with MEK inhibitor (MEKi) for 48 h. m, Western blot of R^TT YUMM1.7^OVA depicting COX2 protein levels upon treatment with RAF inhibitor (RAFi) or MEKi for 48 h. For gel source data see Supplementary Fig. 1. Experiment repeated 2 independent times. n, Heatmap of scaled gene expression in YUMM1.7^OVA R^TT cancer cells upon treatment with MEKi in vivo (n = 5 tumors per condition). o, ISG expression in YUMM1.7^OVA R^TT Ptgs2 KO cell line compared to N^TT and R^TT CTRL by RT-qPCR (n = 4 technical replicates). All expression values are depicted as the log₂FC of the expression of each gene compared to N^TT. p, Quantification of IFN-β by ELISA from YUMM1.7^OVA R^TT CTRL and R^TT Ptgs2 KO tumors grown in Rag2^−/− mice normalized to tumor weight (n = 6 tumors/group). q, Quantification of PGE₂ by ELISA from YUMM1.7^OVA R^TT CTRL and R^TT IRF3/7 tumors grown in Rag2^−/− mice (n = 3 tumors/group). r, Quantification of IFN-β and PGE₂ by ELISA from N^TT and R^TT variants of M249, LOX and H358 cell lines. For IFN-β analysis, tumors were grown in NSG mice and normalized to tumor weight (n = 3 tumors/group). For PGE₂, supernatants from in vitro cell lines were analyzed (n = 2 biological replicates/group). s, Western blot of R^TT, N^TT and N^TT NRAS variants of A375 human melanoma cell lines depicting COX2 protein levels upon treatment with RAFi for 48 h. For gel source data see Supplementary Fig. 1. Experiment performed once. Bar graphs depict the mean ± s.e.m. Statistical analysis was performed with a two-tailed student’s t-test in a, d, k, p, q and r and one-way ANOVA with Tukey’s multiple comparison in h. p-value in e was calculated using a Log-rank Mantel Cox test. ns = not significant. ND = Not detected. Source Data

Extended Data Fig. 6. High PGE₂ and low IFN-I instruct an immune evasive TME.

a, Gating strategy for the identification of intratumoral myeloid populations. b, Flow cytometry myeloid characterization pre and 72 h post-ACT (n = 6 for R^TT ± ACT, IRF37 R^TT ± ACT, n = 5 for N^TT ± ACT, R^TT Ptgs2 KO + ACT, n = 4 R^TT Ptgs2 KO). c, Flow cytometry characterization of dendritic cell populations pre and 72 h post-ACT, (n = 5 for N^TT, R^TT Ptgs2 KO ± ACT, IRF37 R^TT + ACT, n = 6 for N^TT + ACT, R^TT ± ACT, IRF37 R^TT). d, Relative frequency of cell types across conditions in YUMM1.7^OVA (scRNA-seq), for R^TT CTRL the R^TT mCherry sample was used. e, Heatmap of scaled gene expression (scRNA-seq) between N^TT, R^TT CTRL, R^TT Ptgs1/2 KO and R^TT IRF3/7 for individual cell clusters. f, Flow cytometry characterization of SIINFEKL peptide on MHCI of dendritic cells isolated from tumors and pulsed ex vivo with SIINFEKL peptide (n = 6 tumors/group) and MFI quantification. g, Scheme outlining in vivo NK cell depletion in Rag2^−/− mice harboring YUMM1.7^OVA R^TT Ptgs2 KO tumors. h, Representative plot depicting NK1.1⁺ cells in NK cell-depleted vs CTRL R^TT Ptgs2 KO tumors measured by flow cytometry (left) and quantification of cDCs (n = 4 CTRL and n = 3 anti-NK1.1 treated tumors) (right). i, Representative BLI images (left) and quantification of CD8⁺ OT-1^Luc T cell infiltration by BLI (right), (n = 3 mice for N^TT, n = 5 mice for all other groups). j, Tumor response to ACT (n = 5 mice/group). Arrow indicates day of ACT. Bar graphs and growth curves depict the mean ± s.e.m. Data in i depicts the mean ± s.e.m. Statistical analysis was performed with a two-tailed unpaired student’s t-test in h. A one-way ANOVA with Tukey’s multiple comparison in f. Two-way ANOVA with Tukey’s multiple comparison in i. ns = not significant. Source Data

Extended Data Fig. 7. Type I and type II IFNs determine the inflammatory state.

a, b, Overall survival of melanoma patients stratified according to the TME-COX and TME-IRF3/7 signatures (Supplementary Table 4) (left) and correlation of intratumoral CD8⁺ T cell signature (right) in the Gide et al. RNA-seq data-set. PCC=Pearson correlation coefficient. c, TME-COX and TME-IRF3/7 signatures projected on the myeloid UMAP from baseline tumors (split for responder and non-responders to TIL therapy) from the Barras et al. patient cohort (above) and individual score across different myeloid populations (below). d, Gene set enrichment analysis (GSEA) of N^TT and R^TT tumors pre and post-ACT based on scRNA-seq analysis. Normalized enrichment score (NES). Multiple correction testing with false discovery rate. e, Pathway enrichment analysis of the top variable genes in the immune tumor microenvironment (TME) from N^TT and R^TT tumors pre and post-ACT analyzed by scRNA-seq. 1, 2 and 3 depict each gene cluster identified. Benjamini-Hochberg correction for multiple testing. f, TME characterization of YUMM1.7^OVA R^TT Ptgs2 KO tumors pre and 72 h post-ACT upon αIFN-γ and αIFNAR1 treatment (n = 5 tumors/group, except n = 4 for isotype and αIFNAR + ACT). g, Trajectory analysis and pseudotime of the MoMac compartment of YUMM1.7^OVA N^TT and R^TT tumors. h, Density plots of F4/80⁺ macrophages after exposure of BM-derived monocytes to PGE₂ for 48 h (left) and quantification (right) (n = 3 replicates per condition). i, Scheme outlining the intratumoral (i.tu.) transfer of CD45.1⁺ BM-derived monocytes into N^TT and R^TT tumors injected into CD45.2⁺ Rag2^−/− mice (left) and inflammatory monocytes frequencies 3 days after transfer (right) (n = 5 tumors/group). j, Scheme outlining the knockout of EP2 (Ptger2) and EP4 (Ptger4) in OT-1 T cells and i.v. injection into YUMM1.7^OVA R^TT tumor-bearing Rag2^−/− mice (left) and tumor growth (n = 6 mice/group) (right). Arrow indicates the day of ACT. k, Expression of Ptger2 and Ptger4 across populations from the YUMM1.7^OVA scRNA-seq. Bar graphs depict the mean ± s.e.m. Statistical analysis was performed with a two-tailed unpaired student’s t-test in h and i. p-value in a and b was calculated using a Cox’s proportional hazards model (survival) and with a two-sided Pearson’s correlation (correlations). Source Data

Extended Data Fig. 8. PGE₂ suppresses myeloid responsiveness to IFN-I.

a, Tumor growth of YUMM1.7 (left) and YUMM3.3 (right) R^TT in Itgax-Cre (CTRL) (n = 7 mice for YUMM3.3 and n = 8 for YUMM1.7) or CD11c^Cre(Itgax-Cre)Ptger2^−/−/Ptger4^fl/fl treated with anti-CD8 to deplete CD8⁺ T cells (n = 5 mice for YUMM3.3 and n = 6 for YUMM1.7) or isotype control (n = 7 mice for YUMM3.3 and n = 10 for YUMM1.7). b, TME characterization (flow cytometry) of R^TT tumors in CD11c^Cre-Ptger2^−/−Ptger4^fl/fl mice (KO^EP2/EP4) or CD11c^Cre control mice (CTRL) (n = 8 tumors/group). c, Quantification of Ly6A⁺ cells after exposing BM-derived Ly6C⁺ monocytes to conditioned media (CM) from cancer cell lines ± COX1/2 inhibitor (COX1/2i, indomethacin) during media conditioning ± αΙFNAR1 during 48 h of culture (n = 3 biological replicates per condition). d, MFI quantification of AXL, MHC-I and CD86 (flow cytometry) of BM-DCs treated with fresh media (C) or CM from N^TT, R^TT and R^TT IRF3/7 cells for 24 h ± αΙFNAR1 or isotype control (n = 3 biological replicates per condition). e, Heatmap of scaled expression of ISGs in BM-DCs exposed to CM from R^TT IRF3/7 treated with COX1/2i (indomethacin) or untreated, in the presence of αΙFNAR1 or isotype control measured by RT-qPCR (n = 4 technical replicates). f, Heatmap of scaled ISG expression in BM-DCs exposed to CM from N^TT and R^TT treated with MEKi or untreated, in the presence of αΙFNAR1 or isotype control (n = 4 technical replicates) measured by RT-qPCR. g, Scheme outlining the culture of human monocyte models to CM from cancer cell lines ± COX1/2i during media conditioning (left) and heatmaps of scaled ISG expression (right). h, Quantification of AXL⁺ BM-DCs treated with IFN-β and PGE₂ (flow cytometry) (left) (n = 3 biological replicates) and heatmap of scaled ISG expression measured by RT-qPCR (right) (n = 4 technical replicates). Bar graphs and growth curves depict the mean ± s.e.m. Statistical analysis were performed with a two-way ANOVA with Tukey’s multiple comparisons test in a, a two-tailed unpaired student’s t-test (APCs, cDC1 and infl.monocytes) and two-tailed Mann Whitney U (total cDCs and infl.cDC2s) in b, one-way ANOVA with Tukey’s multiple comparison in c, d and h. ns = not significant. Source Data

Extended Data Fig. 9. Meta-analysis of retrospective clinical studies.

a-c, Forest plot of pooled odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CI) across included studies of patients receiving ICB with and without concomitant non-steroidal anti-inflammatory drugs (NSAIDs) medication. Number of participants are included in each figure panel. a, Odds ratios comparing overall response rates. b, Hazard ratios comparing progression-free survival. c, Hazard ratios comparing overall survival. I²: I-squared; P: probability. d, PRISMA flow diagram of literature search and study selection process for meta-analysis. Statistical analysis was performed with a random effects model, data are presented as mean values ± 95% confidence interval. See also Supplementary Tables 5 and 6.

Extended Data Fig. 10. Pharmacological inhibition of PGE₂ and type I IFN modulators resensitize cross-resistant tumors to immunotherapy.

a, Relative frequency of each cell type across conditions (scRNA-seq). b, Scoring of the Duong et al. inflammatory signature in R^TT COX2i-treated tumors. c, Heatmap of scaled gene expression (scRNA-seq) in YUMM1.7 ^OVA R^TT vehicle-treated tumors (CTRL) and R^TT celecoxib (COX2i)-treated tumors (n = 3 tumors were pooled/group). d, Response of YUMM1.7^OVA R^TT tumors to vehicle + adoptive T cell transfer (ACT) (n = 7 mice), celecoxib (n = 4 mice), celecoxib + ACT (n = 6 mice) and etoricoxib ± ACT (n = 7 mice/group). Red arrow and box indicate COX2i treatment stop. e, UMAP of scRNA-seq of CD45⁺ cells from YUMM1.7^OVA R^TT mice treated with vehicle or COX2i + 5-AZA isolated 72 h post-ACT (n = 3 tumors pooled/group). f, Relative frequency of each cell type across conditions in e (scRNA-seq). g, Scoring of the Duong et al. inflammatory signature in COX2i + 5-AZA treated tumors. h, Response of YUMM1.7^OVA R^TT tumors to 5-azacytidine (5-AZA) (n = 4 mice), 5-AZA + ACT (n = 7 mice), vehicle+ACT (n = 8 mice), Flt3L+ACT (n = 7 mice), COX2i+ACT (n = 9 mice), COX2i + 5-AZA + ACT (n = 7 mice) and COX2i+Flt3L+ACT (n = 8 mice). i, Representative image of IF staining of vehicle-treated and COX2i+Flt3L R^TT YUMM1.7^OVA tumors 96 h post-ACT (n = 2 tumors per condition). Scale bar = 1000 µm, zoom-in = 50 µm. Black arrows indicates day of ACT. Source Data

Extended Data Fig. 11. Combination therapies in melanoma, lung, pancreatic and colorectal mouse cancer models.

a, Scheme outlining the re-injection of YUMM1.7^OVA R^TT cells into tumor-free responder mice from Fig. 5e (left) and tumor growth of re-challenged mice (n = 4 mice for COX2i + Ftl3L, n = 3 COX2i and n = 2 COX2i + 5-AZA) or naive control mice (n = 5 mice). b, Scheme outlining the injection of YUMM3.3 R^TT tumors into C57BL/6 mice and the treatment regimen with COX2i, Flt3L, 5-AZA and anti-PD1/CTLA-4 (ICB) or isotype control. c, Response to control vehicle treatment or vehicle + ICB treatment of YUMM3.3 R^TT tumors (n = 6 mice). d, Response to single treatments and ICB combination treatments of YUMM3.3 R^TT tumors. COX2i, Flt3L, 5-AZA + ICB, COX2i+ICB and Flt3L+ICB (n = 8 mice), COX2i + 5-AZA + ICB and COX2i+Flt3L+ICB (n = 10 mice). e, Survival plot. f, Scheme outlining the injection of CT-26 R^TT tumors into BALB/c mice and the treatment regimen with COX2i, Flt3L, 5-AZA and anti-PD1 (ICB) or isotype control. g, Response to treatment of CT26 R^TT tumors. Vehicle and Vehicle+ICB (n = 8 mice), COX2i + 5-AZA + ICB and COX2i+Flt3L+ICB (n = 9 mice). h, Survival plot. i, Scheme outlining the intrapancreatic injection of EPP2^Luc cells into C57BL/6 mice and the treatment regimen with COX2i, Flt3L and anti-PD1 (ICB) or isotype control. j, Left, representative BLI images of EPP2^Luc tumor burden and right, quantification of control (n = 5 mice), anti-PD1 (n = 6 mice), anti-PD1 + COX2i + Ftl3L (n = 6 mice). k, Survival plot of j. l, Scheme outlining the injection of KPAR tumors into C57BL/6 mice and the treatment regimen with COX2i, Flt3L and anti-PD1 (ICB) or isotype control. m, Response to treatment of KPAR tumors, vehicle ± ICB (n = 6 mice) and ICB + COX2i + Ftl3L (n = 8 mice). n, Survival plot of m. o, Scheme summarizing the role of inflammatory monocytes in T cell restimulation and the functional convergence of PGE₂ and IFN-I to determine an inflammatory TME, T cell restimulation and immunotherapy response. Bar graphs depict the mean ± s.e.m. Statistical analysis was performed with a one-way ANOVA with Tukey’s multiple comparisons test in j. Log-rank Mantel Cox test in h, k, n. ns = not significant. Source Data