Anti-inflammatory drugs remodel the tumor immune environment to enhance immune checkpoint blockade efficacy

Abstract

Identifying strategies to improve the efficacy of immune checkpoint blockade (ICB) remains a major clinical need. Here, we show that therapeutically targeting the COX-2/PGE₂/EP2-4 pathway with widely used non-steroidal and steroidal anti-inflammatory drugs synergized with ICB in mouse cancer models. We exploited a bilateral surgery model to distinguish responders from non-responders shortly following treatment and identified acute IFN-γ-driven transcriptional remodeling in responder mice, which was also associated with patient benefit to ICB. Monotherapy with COX-2 inhibitors or EP2-4 PGE₂ receptor antagonists rapidly induced this response program and, in combination with ICB, increased the intratumoral accumulation of effector T cells. Treatment of patient-derived tumor fragments from multiple cancer types revealed a similar shift in the tumor inflammatory environment to favor T cell activation. Our findings establish the COX-2/PGE₂/EP2-4 axis as an independent immune checkpoint and a readily translatable strategy to rapidly switch the tumor inflammatory profile from cold to hot.

Introduction

Therapeutically targeting immune inhibitory checkpoints through the blockade of PD(L)-1 and CTLA-4 has led to unprecedented and durable responses in multiple cancer types (1). Despite this, many patients still fail to respond to immune checkpoint blockade (ICB) due to poorly understood mechanisms of intrinsic and acquired resistance (2). Moreover, life-threatening immune-related adverse events (irAEs) often develop in a significant proportion of patients and remain a major obstacle for the use of ICB, especially in adjuvant and neoadjuvant settings (3). There is therefore an urgent clinical need to better understand, and therapeutically exploit, mechanisms of resistance through the design of modified treatment regimens including combination therapies (4).

Tumors are infiltrated by a diverse group of immune and non-immune cells whose functions can have both pro- and anti-tumorigenic effects. The balance of these opposing inflammatory mediators plays a pivotal role in determining tumor progression and ICB treatment outcome (5,6). Manipulating the flavor of tumor inflammation thus represents an attractive strategy by which ICB efficacy could be improved, either through combinations with cancer therapies known to have immunostimulatory effects or through direct inhibition of pro-tumorigenic inflammation (4). Cyclooxygenase (COX)-2 and one of its downstream enzymatic products prostaglandin E2 (PGE₂) are commonly upregulated in cancer and implicated in multiple aspects of malignant tumor growth such as proliferation, angiogenesis, invasion, and metastasis (7). PGE₂ has also been shown to have pleiotropic effects on immune cell function, and it is increasingly thought that its tumor-promoting functions occur by shaping the tumor immune environment. Accordingly, cancer cell expression of COX-2 and production of PGE₂ play a dominant role in tumor immune evasion by directly inhibiting cytotoxic cell function and subsequent adaptive immune responses, in favor of tumor-promoting inflammation (5,8–10). In addition, COX-2 expression anti-correlates with the expression of multiple inflammatory mediators characteristic of so-called ‘hot’ tumors (5) and associated with responses to ICB such as CXCL9, CXCL10, granzyme B and interferon (IFN)-γ (11,12).

The COX-2/PGE₂ pathway is therefore a promising target for enhancing the efficacy of ICB and can be therapeutically targeted by a plethora of widely used non-steroidal anti-inflammatory drugs (NSAIDs), such as aspirin and selective COX-2 inhibitors. Corticosteroids are also thought to exert part of their anti-inflammatory effects through inhibiting COX-2 (13). Furthermore, corticosteroids are commonly administered for pain management and treatment-related side-effects in cancer patients, including ICB-induced irAEs (3). Past trials have tested NSAIDs as monotherapy, or in combination with cytotoxic therapies, but failed to show a significant improvement in overall survival (7). Instead, more recent retrospective studies have suggested that NSAIDs may enhance the overall survival of patients receiving ICB (14,15). Rather intriguingly, retrospective analysis has also shown that the overall response rate of patients who discontinued ICB and received corticosteroids is similar, and in some cases even higher, than that of patients maintained on ICB (16–19).

We have previously shown that genetically targeting the COX-2/PGE₂ axis in cancer cells leads to spontaneous immune-dependent tumor control (5,8,10) and shifts the tumor microenvironment towards one permissive to ICB responses. Other recent studies also suggest a benefit of therapeutically targeting COX-2/PGE₂ to improve the efficacy of immunotherapy including ICB (8,20–23) although this remains to be tested using clinically applicable regimens. More importantly, the mechanisms underlying the putative synergy between COX-2 inhibition and ICB are ill-defined. Here, we set out to test the hypothesis that widely used anti-inflammatory drugs can be repurposed to modulate the intratumoral immune profile and heighten the efficacy of ICB. We show that selectively inhibiting the COX-2/PGE₂/EP2-4 axis can lead to rapid remodeling of the tumor environment of both murine and human tumors towards one favorable to ICB efficacy.

Results

Cancer cell-intrinsic COX-2 expression confers resistance to immune checkpoint blockade

Genetic ablation of cancer cell-intrinsic COX-2 results in immune-dependent tumor growth control across a diverse set of murine cancer models (5,8,10). In the CT26 colorectal and poorly immunogenic 4T1 breast cancer models, T cells delay the growth of COX-deficient cancer cells but eventually most mice succumb to progressive tumor growth (8). We took advantage of these two experimental models to test the hypothesis that cancer cell-intrinsic ablation of COX-2 sensitizes tumors to ICB, and treated mice with 4T1 or CT26 parental (COX2^WT) or COX-2-deficient (COX2^KO) tumors (Supplementary Fig. S1A and S1B) with anti-PD-1 (αPD-1). Mice with COX-2^WT 4T1 or CT26 tumors were poorly responsive to PD-1 blockade whereas several mice bearing COX2^KO tumors experienced complete regressions within two weeks following treatment and enhanced survival (Fig. 1A and Supplementary Fig. S1C). Restoring COX-2 expression in COX2^KO 4T1 cells (COX2^REST) by retroviral transduction re-established PGE₂ production (Supplementary Fig. S1A and S1B) and resistance to αPD-1 therapy (Fig. 1A), demonstrating that tumor-intrinsic COX-2 activity may be a powerful resistance mechanism to ICB.

Figure 1. Inhibition of the COX-2 pathway via genetic ablation or anti-inflammatory drug treatment synergizes with ICB to overcome immunotherapy resistance.

Mice inoculated with parental (COX2^WT), COX-2-deficient (COX2^KO) or COX-2-restored (COX2^REST) cancer cell lines were treated twice weekly with αPD-1 from day 7 post-cancer cell inoculation when tumors were 4.0 ± 1.3 mm in mean diameter. (A) (Left panel) Waterfall plot showing % change in tumor size 3 weeks post-treatment (n=4-21 per group) and % of tumor rejections at experimental endpoint in mice bearing COX2^WT, COX2^KO or COX2^REST 4T1 breast tumors treated with or without αPD-1. Data pooled from 4 independent experiments. (Right panel) Kaplan-Meier survival plots of mice bearing COX2^WT (n=16), COX2^KO (n=20) or COX2^REST (n=4) 4T1 breast cancer cell lines and treated with αPD-1. (B) Mice inoculated with parental (COX2^WT) CT26 colorectal cells were treated with αPD-1 and/or daily anti-inflammatory drugs (CXB or MP+P) from day 7 post-cancer cell inoculation when tumor mean diameter was 3.3 ± 1.2 mm. (C) Individual growth profiles of CT26 colorectal tumors treated with vehicle (n=5), αPD-1 (n=9), CXB (n=5) or αPD-1 & CXB (n=10). Time of treatment initiation indicated by a black arrow on each growth profile. (D, E) (Left panels) Waterfall plot showing % change in tumor size 2- or 3-weeks post-treatment and % of tumor rejections at experimental endpoint of mice bearing CT26 colorectal tumors. (Right panels) Kaplan-Meier survival plots of mice bearing CT26 colorectal tumors treated with vehicle, αPD-1 and/or CXB or MP+P (n= 11-43 per group). Data pooled from at least 4 independent experiments. (F) Frequency of tumor-infiltrating immune cells as a % of total leukocytes in CT26 colorectal tumors at day 7 post-treatment (n= 5 per treatment group). (G, H) Representative image of multiplex immunofluorescence staining of CD8⁺, CD4⁺ and Foxp3⁺ cells in whole tumor sections (G) and quantitation of CD8⁺ T cells in 20 distinct tumor areas pooled from 3-4 mice bearing CT26 colorectal tumors and treated with vehicle, αPD-1 and/or CXB or MP+P for 7 days (H). Red line = mean of all tumor areas, Blue line = mean of whole tumor area. P-value by one-way ANOVA (A, D, E) representing comparisons of major experimental groups and their respective controls only. Log-rank (Mantel-Cox) test of monotherapies versus vehicle or combination versus either of the monotherapies (A, D, E). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Anti-inflammatory drugs targeting the COX-2 pathway synergize with ICB to promote tumor control

We next determined whether therapeutic inhibition of COX-2 could similarly improve PD-1 blockade efficacy. To test this in a clinically relevant setting, we used celecoxib (CXB), a selective COX-2 inhibitor widely prescribed for managing certain inflammatory disorders (www.medicines.org.uk/emc/product/5533/smpc). To control both the dose and timing of CXB administration, we developed a vehicle formulation that led to its complete solubilization as oppose to previous studies providing anti-inflammatory drugs ad libitum in the drinking water, chow or subcutaneously (8,22,23). We treated mice with established CT26 tumors with systemic αPD-1 in combination with a daily oral gavage of CXB (Fig. 1B) at a dose of 30mg/kg, equivalent to what is considered safe and well tolerated in patients with inflammatory conditions (24). The combination of αPD-1 plus CXB led to greater tumor growth control, with up to 65% of mice exhibiting complete tumor regressions and marked survival benefit compared to either αPD-1 or CXB monotherapy alone (Fig. 1C and D).

More than 50% of patients receiving treatment with combinations of ICB (typically antibodies targeting the PD(L)-1 and CTLA-4 pathways) suffer severe irAEs. Most of these patients are withdrawn from ICB treatment and receive systemic corticosteroids to resolve their irAEs (16,17). One way by which corticosteroids exert their anti-inflammatory effects is through inhibition of the COX-2/PGE₂ pathway (13). Indeed, we found that corticosteroids profoundly suppress PGE₂ production by cancer cells to a similar extent as CXB (Supplementary Fig. S1D). Thus, we hypothesized that corticosteroids might also improve the efficacy of ICB. Using the same experimental approach as for CXB, we administered αPD-1 to mice with established CT26 tumors in combination with daily treatment with systemic methylprednisolone for five days followed by daily oral prednisolone for a total of three weeks (MP+P) (Fig. 1B), mimicking a regimen commonly used to treat patients suffering from irAEs (25). Notably, the combination of MP+P and PD-1 blockade led to increased tumor eradications and prolonged survival compared to αPD-1 or MP+P monotherapies (Fig. 1E; Supplementary Fig. S1E). To confirm corticosteroids were exerting their expected pharmacological activity with this dosing regimen, we monitored the expression of genes directly regulated downstream of the glucocorticoid receptor (26,27). Following five days of treatment with methylprednisolone, the levels of Tat, Igfbp1 and the gene encoding for the glucocorticoid receptor itself, Nr3c1, were all altered (Supplementary Fig. S1F). Consistent with its anti-inflammatory properties, methylprednisolone also dampened the expression of Il1b and Il6 in the liver (Supplementary Fig. S1F), and intratumorally (Supplementary Fig. S1G) (13). Given corticosteroid management of irAEs is more frequently prescribed to cancer patients treated with dual PD-1 and CTLA-4 blockade, we next tested the effect of combined therapy with αPD-1/αCTLA-4 and MP+P. In agreement with previous human and mouse studies (28,29), the dual ICB combination (Supplementary Fig. S1H) was more potent than single treatment with αPD-1 (Fig. 1A). Still, the addition of MP+P led to a trend in further tumor control, enhanced survival and promoted complete tumor regressions in 85% of animals (Supplementary Fig. S1H).

As methylprednisolone was recently shown to inhibit the efficacy of ICB in a dose-dependent manner (30), we tested PD-1 blockade in combination with a higher dose of corticosteroids. The addition of high dose MP+P to αPD-1 promoted greater tumor control when assessed at three weeks post-treatment compared to αPD-1 monotherapy though the combination did not increase the fraction of complete tumor eradications nor mouse survival (Supplementary Fig. S1I). Notably, the efficacy of αPD-1 & high dose MP+P was fully lost in immune-deficient NSG mice, excluding the possibility that tumor control was independent of the immune system (Supplementary Fig. S1I). Concomitant administration of a different corticosteroid, dexamethasone, similarly improved PD-1 treatment (Supplementary Fig. S1I). Finally, mice which had eradicated tumors following αPD-1 treatment with either CXB or MP+P spontaneously rejected tumor re-challenges without further treatment, indicating that the development of long-term immunity was not compromised by the use of CXB or MP+P (Supplementary Fig. S2A). In conclusion, this data demonstrated that non-steroidal anti-inflammatory drugs, as well as corticosteroids widely used for their immunosuppressive effects, can paradoxically enhance the efficacy of ICB in preclinical cancer models.

Anti-inflammatory drugs in combination with ICB alter the molecular but not the cellular tumor inflammatory landscape

Together, these results suggested that anti-inflammatory drugs can actually foster the type of inflammation that favors immune-mediated cancer control. To test this hypothesis, we characterized the tumor immune cell infiltrate by multi-parameter flow cytometry. To identify the causal basis for the enhanced tumor control observed following combination therapy, and to avoid potential confounding effects of analyzing tumors of different sizes, we examined size-matched tumors collected one week post-treatment with αPD-1 with or without the addition of CXB or MP+P (Supplementary Fig. S2B). We found no overt changes in the overall tumor infiltrate composition in either myeloid or lymphoid compartments across the different treatment arms (Fig. 1F). Analysis of tumor sections by immunofluorescence also failed to reveal clear differences in the abundance and spatial distribution of CD8⁺, CD4⁺ T cells or Foxp3⁺ regulatory T cells (Fig. 1G and H; Supplementary Fig. S2C). In spite of the lack of alterations in overall leukocyte composition, the transcript levels of mediators associated with COX-2-driven cancer-promoting inflammation such as IL-6 and IL-1β (5,8) were reduced in tumors from animals receiving the combination of αPD-1 with CXB or MP+P, although the difference for IL-6 did not reach statistical significance in the latter group (Supplementary Fig. S2D).

Transcriptomic profiling using a bilateral tumor system uncovers an early IFN-driven inflammatory program associated with ICB responses

We hypothesized that a possible explanation for the lack of noticeable changes in leukocyte composition in mice receiving αPD-1 plus CXB could be due to the inherent dichotomy in responses across treatment groups. Indeed, despite the remarkable synergy between ICB and anti-inflammatory drugs, we consistently found that a fraction of animals remained unresponsive to treatment across all our experimental systems, similar to what is seen in patients (28). We reasoned that the identification of primary mechanisms underpinning treatment synergy would be greatly confounded by this heterogeneity in responses. To overcome this limitation, we developed a bilateral tumor surgery model that would allow us to distinguish responders from non-responders early on following treatment. In this model, one of the two tumors in mice receiving αPD-1 or αPD-1 plus CXB was surgically removed one week post-treatment for RNA sequencing analysis, while the contralateral tumor continued to be monitored to determine treatment outcome (Fig. 2A). In agreement with recent studies exploiting a similar experimental setup (31,32), we established that responses to αPD-1 or αPD-1 plus CXB were concordant between contralateral tumors. Indeed, in 90% of mice, both tumors in a single mouse grew either progressively or were completely rejected, irrespective of treatment (Supplementary Fig. S3A; Supplementary Table S1). We were therefore able to infer with high confidence the response outcome of the surgically resected tumors based on the progressive (non-responder, NR) or regressive (responder, R) growth of the contralateral tumor. Consistent with our previous data, the addition of CXB improved the efficacy of αPD-1 in a large proportion of mice, however a substantial fraction remained unresponsive to either regimen (Fig. 2B).

Figure 2. Bilateral surgery model uncovers a distinct immune molecular landscape associated with response to ICB.

(A) Mice were inoculated bilaterally with CT26 colorectal cells and treated with αPD-1 alone or in combination with CXB from day 7 post-cancer cell inoculation when tumors were 4.0 ± 1.0 mm in mean diameter. Right flank tumors were surgically excised at day 7 post-treatment and left flank tumor growth was monitored until experiment end to determine response outcome. (B) (Left panel) Waterfall plot showing % change in tumor size 2 weeks post-treatment and % of tumor rejection at experimental endpoint in remaining contralateral tumors (n=16-17 per group). P-value by Mann-Whitney U Test. (Right panel) Kaplan-Meier survival plots of the bilaterally CT26 implanted mice treated with αPD-1 alone (n=17) or combined with CXB (n=16). P-value by Log-rank (Mantel-Cox) test. (C) Hierarchical clustering and heatmap representation of top differentially expressed genes between responder (in red) and non-responder (in black) clusters obtained by non-negative matrix factorization k-means consensus clustering. (D) Dot plot representation of top 10 significantly differentially enriched pathways in samples belonging to the responder and non-responder clusters identified by GSEA of Hallmark gene sets. q-value: False discovery rate. Count: number of genes contributing to the enrichment score. (E) Heatmap representation of the abundance (score) of 10 cell populations, angiogenesis and Immune score in samples from the responder (R) and non-responder (NR) clusters, calculated using the MCP-counter and Consensus^TME methods. P-value by Mann-Whitney U Test. (F) Bar plot representation of significant enrichment of the AIR program in responder (R, complete and partial responders) in comparison to non-responder (NR, stable disease and progressive disease) patients with either on or pre-treatment with ICB. Gene ratio denotes the % of genes within the AIR program contributing to the enrichment score. (G) Enrichment plot of the AIR program genes showing significant enrichment in αPD-1 & CXB versus αPD-1 treatment in CT26 tumors day 7 post-treatment. (H) Volcano plot of predicted upstream regulators by IPA based on the differentially expressed genes between αPD-1 & CXB and αPD-1 treatment in CT26 tumors day 7 post-treatment. Significantly activated and inhibited upstream molecules are highlighted in red and blue, respectively. ES: Enrichment scores. NES: Normalized enrichment scores. FDR: False discovery rate. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Unsupervised k-means clustering on the transcriptome of all surgically resected tumors from mice treated with either αPD-1 or αPD-1 plus CXB revealed two major transcriptional programs that were significantly enriched in samples originating from either R or NR mice (p=0.038, two-tailed Fisher’s exact test, Fig. 2C; Supplementary Fig. S3B). Amongst the top differentially upregulated genes in the R cluster, many were characteristic of cytotoxic T cell activity including Cd8a, Cd8b1, Gzmd and Ifng (Fig. 2C; Supplementary Fig. S3C). To further characterize the biological processes underlying the transcriptional changes defining R and NR clusters, we performed gene-set enrichment analysis (GSEA) using the MSigDB hallmark gene set collection. This analysis showed profound enrichment of the ‘Interferon gamma response’ as well as many other immune-related and inflammatory pathways in the R cluster (Fig. 2D; Supplementary Table S2). Conversely, the NR cluster was enriched in gene sets associated with pro-tumorigenic processes such as ‘Epithelial mesenchymal transition’, ‘G2M checkpoint’ or ‘Myc targets’ whose expression was recently associated with lack of responses in melanoma patients receiving single or combination ICB (12). Likewise, Ingenuity Pathway Analysis (IPA) revealed enrichment of many genes within the IFN-γ pathway and both IFN-γ and STAT1 were predicted as the main upstream regulators of the R cluster (Supplementary Fig. S3D and S3E; Supplementary Table S3).

Next, we inferred the cellular infiltrate composition of tumors from R and NR clusters using the microenvironment cell populations (MCP)-counter (33) and Consensus^TME (34) methods. In agreement with the tumor fate, this analysis revealed a highly significant enrichment in CD8, cytotoxic, T and NK cell populations as well as in the total Immunoscore which represents the total level of immune infiltration in each tumor sample (34) within the R cluster (Fig. 2E; Supplementary Fig. 3F). Conversely, the NR cluster was enriched in signatures of endothelial cells, fibroblasts and angiogenesis. Thus, an unbiased approach to examine the transcriptome of on-treatment responders early following treatment uncovered the induction of an intratumoral molecular program characterized by the upregulation of multiple IFN-driven genes and infiltration by immune populations typically associated with responses to ICB in patients.

Figure 3. Inhibition of PGE₂ synthesis or downstream receptor signaling synergizes with ICB to promote the molecular program associated with response to immunotherapies.

(A) Experimental model representing the inoculation of cancer cell lines in wild-type mice treated with 4 doses of αPD-1 and/or twice daily doses of a mix of EP2 and EP4 receptor antagonists (EPAT) from day 7 post-cancer cell inoculation when tumors were 4.8 ± 0.7 mm in mean diameter. (B) (Left panel) Waterfall plot showing % change in tumor size 2 weeks post-treatment and % of tumor rejection at experimental endpoint of 5555 melanoma bearing mice treated with vehicle, αPD-1 and/or EPAT (n= 10-20 per group). Data represents three independent experiments. P-value by one-way ANOVA representing comparisons of major experimental groups and their respective controls only. (Right panel) Kaplan-Meier survival plots of experimental groups. P-value by Log-rank (Mantel-Cox) test of monotherapies versus vehicle or combination versus either of the monotherapies. (C) Enrichment plot of the AIR program genes showing significant enrichment in EPAT or CXB versus vehicle treated 5555 melanoma tumors day 2 or day 7 post-treatment, respectively. (D) Hierarchical clustering and heatmap representation of differentially expressed genes upon treatment with αPD-1 and/or EPAT in comparison to vehicle controls in 5555 melanoma tumors day 2 post-treatment. (E) Mean delta Euclidian distance between αPD-1 and/or EPAT compared to vehicle, paired analysis of each treated sample versus vehicle controls. P-value by one-way ANOVA followed by multiple comparisons test. (F) Volcano plot of predicted upstream regulators by IPA based on the differentially expressed genes between each treatment group and vehicle controls in melanoma tumors at day 2 post-treatment. Significantly activated and inhibited upstream molecules are highlighted in red and blue, respectively. (G) Dot plot representation of top significantly differentially enriched pathways in each treatment group in comparison to vehicle treatment in melanoma tumors at day 2 post-treatment as identified by GSEA of hallmark gene sets. q-value: False discovery rate. count: number of genes within each gene set contributing to the enrichment score. *p<0.05, ***p<0.001, ****p<0.0001.

We wondered whether this acute interferon response program that preceded tumor eradication, referred to from now as Acute Interferon Response (AIR) program, would similarly associate with responses in patients treated with ICB. To test this, we interrogated two separate cohorts of αPD-1 treated melanoma patients with available transcriptional profiles from on-treatment tumor biopsies (Supplementary Table S4). In agreement with the murine findings, the AIR program was enriched in on-treatment samples from responders compared to non-responders (Fig. 2F). This association also extended to pre-treatment samples in melanoma and other cancer types including non-small cell lung (NSCLC) and renal cell cancer (RCC) (Fig. 2F; Supplementary Table S4) in line with IFN-γ signaling being a feature of ‘hot’ tumors and a major contributor to ICB efficacy (11,12). In conclusion, a bilateral surgery model was able to capture the heterogeneity in treatment outcomes and identified the early activation of an intratumoral molecular program linked to immunotherapy responses in both mouse and patient tumors.

PGE₂ receptor antagonists phenocopy COX-2 inhibition and synergize with ICB treatment

Having identified a transcriptional response preceding ICB-driven tumor control, we next tested whether the addition of CXB to ICB would induce the AIR program irrespective of treatment outcome. The AIR program was indeed significantly enriched in tumors from mice treated with αPD-1 plus CXB compared to αPD-1 alone (Fig. 2G) in accordance with their increased responses to treatment. Furthermore, IPA on differentially expressed genes (DEGs) between tumors from αPD-1 & CXB versus αPD-1-treated mice also identified IFN-γ as the top upstream regulator (Fig. 2H; Supplementary Table S3). Conversely, PGE₂ and its receptor EP4 (encoded by PTGER4) were among the most inhibited upstream regulators. This data suggested that the heightened IFN-γ response following addition of CXB to PD-1 blockade might occurred as a result of dampening PGE₂ synthesis and downstream signaling through its specific receptors. This hypothesis is in line with our recent finding that PGE₂ promotes cancer immune escape by signaling through its specific receptors EP2 and EP4 on the surface of cytotoxic cells (5).

As well as PGE₂ synthesis, COX-2 is required for the synthesis of multiple inflammatory mediators including leukotrienes, thromboxanes and other prostaglandins with known pro- or anti-tumorigenic functions (7). Thus, therapeutically targeting EP2 and EP4 could represent a more selective approach to limit the immunosuppressive functions of PGE₂ whilst simultaneously sparing cancer restraining-prostaglandins and avoiding potential adverse effects associated with COX-2 inhibition (35). We therefore tested whether selective EP2 and EP4 antagonists would mimic COX-2 inhibition and enhance the efficacy of PD-1 blockade. In an analogous regimen to the one used for dosing CXB by oral gavage, we treated mice with established CT26 colorectal or 5555 Braf^V600E-driven melanoma tumors, on a BALB/c or C57BL/6 background respectively, with αPD-1 in combination with bi-daily doses of a mix of both EP2 and EP4 antagonists (EPAT) (Fig. 3A). Compared to monotherapy treatment with either αPD-1 or EPAT, which did not induce tumor control, their combination led to a significant number of tumor eradications and increased overall survival in both cancer models (Fig. 3B; Supplementary Fig. S4A). Mice which had rejected tumors following either αPD-1 or αPD-1 plus EPAT were resistant to a further re-challenge with parental cells (Supplementary Fig. S4B) indicating the development of long-term immunity, similar to regimens combining immunotherapy with CXB or MP+P. This data suggested that selective COX-2 inhibitors potentiate ICB therapy primarily by inhibiting PGE₂ synthesis and EP2-4 downstream signaling.

To test if the combination of PD-1 blockade and EP2-4 antagonism promoted a shift in the transcriptional landscape of tumors similar to that induced by αPD-1 plus CXB, we treated mice with αPD-1 and/or EPAT and harvested tumors for transcriptional profiling by RNA sequencing. To investigate the temporal kinetics of the changes induced by the single treatments or their combination, we analyzed 5555 melanoma tumors at two and five days on-treatment. As early as two days, the AIR program was significantly enriched in αPD-1-treated mice compared to vehicle (Supplementary Fig. S4C), in line with a reported peak in IFN-γ production two days following αPD-1 treatment (36). Notably, single treatment with EPAT led to the induction of the AIR program at day 2 as well as day 5, an effect that was also observed in CT26 colorectal tumors following CXB monotherapy (Fig. 3C; Supplementary Fig. S4D). This data suggested that inhibition of PGE₂ synthesis or downstream signaling via EP2-4 have rapid and comparable effects on the intratumoral transcriptional landscape to PD-1 blockade. To further examine this possibility, we performed unsupervised hierarchical clustering on genes differentially expressed between tumors from mice treated with either αPD-1, EPAT or their combination compared to vehicle-treated control mice at day 2 post-treatment. Tumors treated with either αPD-1 or EPAT clustered separately from the majority of vehicle-treated tumors and largely intermingled (Fig. 3D), suggesting they had comparable changes in their transcriptional profile. Interestingly, these changes were more pronounced in mice that received the combination of αPD-1 plus EPAT, which clustered furthest from vehicle-treated tumors (Fig. 3D), as demonstrated by their greater Euclidian distance from vehicle (Fig. 3E). These findings were further supported by IPA and GSEA. Indeed, the most highly enriched upstream regulators and hallmark gene-sets, including IFNG and STAT1, were common between all three regimens (Fig. 3F and G; Supplementary Table S3), however, the combination of αPD1 plus EPAT induced the most significant shift (Fig. 3G). Collectively, this data indicated that simultaneous blockade of PD-1 and PGE₂ receptor signaling can acutely remodel the tumor landscape towards a transcriptional profile associated with ICB benefit in mice and humans. Crucially, single targeting of PGE₂ production or signaling induced similar changes, albeit to a lower degree, suggesting inhibition of the COX-2/PGE₂/EP2-4 axis might present a readily available strategy to rapidly render tumors more permissive to anti-cancer effector T cells.

COX-2/PGE₂/EP2-4 inhibition leads to abrupt but transient IFN-γ-driven inflammatory signaling in the tumor microenvironment

To further explore the potential of COX-2/PGE₂/EP2-4 targeting as a means to turn the molecular profile of tumors from cold to hot, we treated tumor-bearing mice with either CXB or EPAT monotherapy twice daily for two days and determined the expression levels of immune mediators classically associated with cancer inhibitory (CI) T cell-inflammation (5) by quantitative PCR (Supplementary Fig. S5A). Unsupervised clustering revealed two major clusters, one of which was significantly enriched in tumors treated with either CXB or EPAT (p<0.0001, two-tailed Fisher’s exact test, Fig. 4A). We found a marked increase in the expression of multiple CI genes (Supplementary Table S1) including Ifng, Cxcl10, Gzmb, Prf1, Tbet, Cd274 (PD-L1) and Il12b in CXB and EPAT-treated melanoma or colorectal tumors compared to control mice (Fig. 4A and B; Supplementary Fig. S5B). Conversely, CXB and EPAT treatments reduced the expression of COX-2-driven cancer promoting (CP) genes such as Vegfa, Il6 and Ptgs2 (Fig. 4A; Supplementary Table S1). As a result, the COX-2-associated inflammatory signature (COX-IS), that integrates pro- and anti-tumorigenic inflammatory factors and negatively associates with ICB benefit in multiple cancer types (5) was significantly reduced by CXB or EPAT treatment (Fig. 4C; Supplementary Fig. S5C). Strikingly, CI mediators including hallmark IFN-γ-stimulated genes encoding CXCL10, PD-L1 and IFN-γ itself were significantly upregulated 4h after a single dose of CXB (Fig. 4D; Supplementary Fig. S5D). This rapid increase in CI genes was highest 4h following treatment and progressively lost over a 24h period without CXB redosing (Fig. 4B). This is consistent with the pharmacokinetics of CXB (www.medicines.org.uk/emc/product/5533/smpc) and implies sustained COX-2 inhibition might be required for maximum synergy with ICB.

Figure 4. COX-2/PGE₂ pathway inhibition acutely and transiently activates IFN-γ signaling in the tumor microenvironment.

(A) Hierarchical clustering and heatmap representation of significantly deregulated immune mediators classically associated with cancer inhibitory or cancer promoting inflammation, top and bottom cluster of genes, respectively, measured by quantitative PCR in melanoma tumors at day 2 post-treatment with CXB or EPAT monotherapy in comparison to vehicle. Pooled data from at least 3 independent experiments. (B) Time course of induction of cancer inhibitory gene expression following the final dose of CXB represented as the fold change of cumulative z-scores relative to vehicle controls in wild-type, immunodeficient or NK-depleted mice bearing melanoma tumors (n=6-20 mice per group from at least 2 independent experiments). (C) COX-IS measured by quantitative PCR in melanoma tumors at day 2 post-treatment with CXB or EPAT monotherapy in comparison to vehicle (n=10-20 mice per group from 3 independent experiments). (D) Induction of Ifng or IFN-γ-stimulated genes 4h after a single dose of CXB in melanoma tumors detected by quantitative PCR and expressed relative to Hprt expression. (E) (Left panel) Waterfall plot showing % change in tumor diameter 2 weeks post-treatment and % of tumor rejections at experimental end-point and (Right panel) Kaplan-Meier survival plots of mice bearing 5555 melanoma tumors in wild-type mice treated with vehicle, αPD-1 and/or CXB or immunodeficient or NK-depleted mice treated with αPD-1 plus CXB (n=4-22 per group from 1-3 independent experiments). P-value by Log-rank (Mantel-Cox) test, showing significance of αPD-1 or αPD-1 plus CXB versus vehicle in wild-type mice (***p<0.0001), or αPD-1 plus CXB in wildtype mice versus αPD-1 plus CXB evaluated in either Rag^-/-, Batf3^-/-, Ifng^-/- or NK cell-depleted mice (***p<0.001). P-value by one-way ANOVA (B, C) and unpaired t-test (D). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

To investigate the cellular and molecular mediators responsible for the expression of CI mediators following CXB treatment, we treated 5555 melanoma-bearing Rag1 ^–/– or Batf3 ^–/– mice, lacking mature T and B cells or type I conventional dendritic cells (cDC1) respectively, with CXB for two days and analyzed their tumors 4h after the last dose. The levels of CI genes in both CXB-treated Rag1 ^–/– or Batf3 ^–/– mice were significantly lower compared to CXB-treated wild-type animals (Fig. 4B; Supplementary Fig. S5E). Likewise, the expression of CI genes was markedly reduced in mice acutely depleted of NK cells, in line with a role for NK cells in orchestrating the adaptive immune response in COX-2-deficient tumors or when rendered insensitive to PGE₂ (5). The rapid induction of CI genes was also lost in IFN-γ-deficient (Ifng ^–/–) mice treated with CXB (Fig. 4B), consistent with the finding that IFN-γ was predicted to be upstream of the transcriptional changes following COX-2 inhibition. Finally, tumor-bearing Rag1 ^–/–, NK cell-depleted, Batf3 ^–/– or Ifng ^–/– mice did not benefit from αPD-1 & CXB treatment (Fig. 4E) uncovering a central role of the NK/cDC1/T cell axis and IFN-γ for the therapeutical benefit of combining inhibitors of the COX-2/PGE₂ pathway with ICB.

COX-2 inhibition dampens cancer-promoting mediators and stimulates cancer inhibitory inflammation in human tumors

Next, we used a novel patient-derived tumor fragment (PDTF) platform (37) to test whether monotherapy with CXB could similarly shape the human inflammatory tumor microenvironment. Tumor specimens obtained from surgical resections from treatment-naïve patients were dissected into tissue fragments and cultured ex vivo in the presence or absence of CXB. The changes in cellular and secretory profiles of these PDTFs were analyzed 48h later by flow cytometry and multi-parameter cytokine and chemokine arrays (Fig. 5A). We tested 17 independent tumors of multiple tissue origins: melanoma (n=7), lung (n=3), colorectal (n=1), ovarian (n=1) and kidney cancer (n=5) (Supplementary Table S5). Six of these produced high levels of PGE₂ (above 10ng/ml, referred to as PGE₂ ^high) upon 48h of culture (Fig. 5B). The cellular immune and non-immune baseline composition of these tumors was heterogeneous, however, PGE₂ levels did not obviously associate with cancer type or cell composition (Supplementary Fig. S5F). As expected, addition of CXB to the culture media significantly inhibited PGE₂ release (Fig. 5C). This was associated with a distinct shift in the cytokine and chemokine profile of PGE₂ ^high compared to PGE₂ ^low PDTFs, in accordance with their more pronounced reduction in PGE₂ levels following treatment (Fig. 5D and E). In line with our murine data, COX-2 inhibition led to a significant increase in the levels of CXCL9 and CXCL10, and concomitant dampening of immunosuppressive factors such as IL-6, IL-10, CXCL1, and CXCL5 (Fig. 5E). Thus, CXB treatment of multiple independent PGE₂-producing human tumors resulted in a rapid switch in the tumor immune environment towards one conducive to T cell accumulation and effector activity.

Figure 5. COX-2 inhibition alters the inflammatory milieu of PDTF by suppressing multiple inflammatory markers and enhancing CXCL9 and CXCL10 production.

(A) Patient-derived Tumor Fragment (PDTF) platform: surgically resected patient tumors are dissected into fragments and cultured in either medium with or without CXB for 48h before analysis. (B) PGE₂ concentration in the supernatant of PDTFs from 17 independent tumors after 48h of culture in medium. (C) PGE₂ concentration in the supernatant of PDTFs after 48h of culture in medium alone or with CXB. P-value by paired t-test. (D) Heatmap showing the change in cytokine and chemokine concentration induced by CXB relative to medium alone. (E) Volcano plot showing the effect size for each factor comparing PGE₂ ^high versus PGE₂ ^low tumors. Effect size (calculated using Hedge’s g) and p-values (two-tailed Mann-Whitney test) of significantly increased and decreased proteins (p<0.05) are highlighted in purple and green, respectively. *p<0.05.

Concomitant COX-2 inhibition during PD-1 blockade enhances T cell effector function

Having identified rapid and distinct changes in hallmark cytokines and chemokines associated with T cell infiltration and effector function following treatment with αPD-1, CXB, or their combination, we next examined the extent to which the activation phenotype of tumor-infiltrating T cells was changed as a consequence. For this, we analyzed on-treatment immune cell infiltrates by multi-color flow cytometry and by cytometry by time-of-flight (CyTOF) at the onset of 5555 melanoma shrinkage post-ICB treatment when tumors were still comparable in size. The overall composition of most lymphoid and myeloid cell subsets was only moderately changed across treatment groups (Fig. 6A) in line with our earlier data in the CT26 colorectal model (Fig. 1F). There was, however, a decrease in macrophage and increase in CD8⁺ T cell relative abundance but not absolute number in the αPD-1 plus CXB-treated mice compared to vehicle and αPD-1 monotherapy treatment (Fig. 6A; Supplementary Fig. S5G). Hierarchical clustering of more than twenty surface and intracellular proteins (Supplementary Table S6) used to discriminate CD8⁺ T cell functional states (38) revealed two distinct expression patterns across treatment groups (Fig. 6B). Notably, the expression levels of a cluster of markers associated with T cell dysfunction such as TOX, TIM3 and LAG3 (38) tended to be lower in mice receiving αPD-1 plus CXB compared to αPD-1 or vehicle-treated mice (Fig. 6B; Supplementary Table S7). Conversely, the expression of proteins associated with activation and cytotoxic T cell function such as Granzyme B, TBET and TCF1, a transcription factor associated with a naïve-like CD8⁺ T cell phenotype required for PD-1 blockade efficacy (38), were higher in CTLs infiltrating αPD-1 plus CXB treated tumors (Fig. 6B). Unsupervised clustering of CD8⁺ T cells into seven cell states (Fig. 6C; Supplementary Fig. S5H and S5I; Supplementary Table S7) further highlighted the phenotypic switch of CD8⁺ T cells in mice receiving αPD-1 plus CXB. Indeed, a cluster of cells expressing high levels of exhaustion markers (Cluster 6, Fig. 6D; Supplementary Fig. S5I) including CD39 and TOX, was proportionately reduced in mice treated with αPD-1 plus CXB (Fig. 6D). Conversely, tumors from this treatment group showed enrichment in a CTL cluster expressing Granzyme B, low levels of EOMES and intermediate levels of exhaustion markers (Cluster 3, Supplementary Fig. S5I and S5J) and an increase in the infiltration of naïve-like CD8⁺ T cells with high TCF1 and CD62L expression and lower PD-1 levels (Cluster 1, Fig. 6E; Supplementary Fig. S5I). This was associated with an expansion of activated CD4⁺ T cells expressing higher levels of CD25, PD-1 and TBET (Fig. 6F) and enhanced IFN-γ production by both CD8⁺ and CD4⁺ T cells (Fig. 6G). Collectively, this analysis indicated that concomitant COX-2 and PD-1 inhibition restrains the intratumoral accumulation of dysfunctional CD8⁺ T cells and heightens T cell effector capacity.

Figure 6. COX-2/PGE₂ signaling inhibition in combination with αPD-1 treatment causes a phenotypic shift in tumor-infiltrating CD8⁺ T cells.

(A) Frequency of tumor-infiltrating immune cells out of CD45⁺ cells in melanoma tumors at day 7 post-treatment, as measured by CyTOF (n=5 per treatment group). P-value by two-way ANOVA. (B) Hierarchical clustering and heatmap representing the mean intensity expression of select surface and intracellular markers associated with intratumoral CD8⁺ T functional states measured by CyTOF. (C) Uniform Manifold Approximation and Projection (UMAP) plots displaying the relative abundance of different CD8⁺ T cell clusters across treatment groups as defined by unsupervised self-organizing map clustering (FLowSOM). (D) Treatment-based comparison of the relative abundance of Cluster 6 within CD8⁺ T cells, which is characterized by high expression of TOX. (E) Treatment-based comparison of the relative abundance of Cluster 1 within CD45⁺ T cells, which is characterized by high expression of CD62L and the stem-like marker TCF1. (F) Bar plots of the median intensity (MI) of PD-1, CD25 and T-bet expression in non-Treg CD4⁺ T cells. (G) Bar plots of the frequency of CD44⁺ IFN-γ⁺ cells within CD8⁺ or CD4⁺ T cells. (H) Heatmap representing log2 fold change in expression of cytokines, chemokines and T cell-surface markers and (I) Paired dot plots representing levels of IL-2, IFN-γ and CXCL10 48h following anti-CD3 and/or CXB treatment of PDTFs from four independent tumors (averaged from 1-2 repeat cultures). (J) Representative FACS plots and paired dot plots of OX40 (% of CD4) or CD137 (% of CD8) expression 48h following anti-CD3 and/or CXB treatment of PDTFs from four independent tumors (averaged from 1-2 repeat cultures). P-value by one-way ANOVA (D, E, F, G, I, J). Data representative of 2 independent experiments with n=5 mice per group (A-G) or of 2 independent PDTF cultures (H-J). *p<0.05, **p<0.01.

Finally, we tested whether CXB treatment could also enhance T cell activation in the human PDTF setting. For this, we cultured PDTFs from four independent PGE₂ ^high tumors with αCD3 in the presence or absence of CXB. Compared to αCD3 stimulation alone, the addition of CXB enhanced the production of the characteristic T cell effector cytokines IL-2 and IFN-γ and the T cell-chemoattractant CXCL10 (Fig. 6H and I), and increased the expression of activation markers OX40 and CD137 on T cells (Fig. 6H and J). Together, our data supported a model whereby inhibition of the COX-2/PGE₂ axis rapidly and acutely shifts the tumor inflammatory landscape to promote the infiltration and activation of anti-tumor effector T cells, ultimately resulting in enhanced responses to immune checkpoint blockade.

Discussion

Inflammation has long been described as a hallmark associated with cancer initiation, progression, recurrence and resistance to mainstream treatments (39). Multiple studies using advanced single-cell analysis of the tumor microenvironment have elegantly characterized the phenotypic and functional diversity of tumor-infiltrating immune cells. Combined with the success of therapies targeting the host immune response, this has highlighted a dual role of inflammation in cancer (6). In recent work, we have identified COX-2 activity and its associated inflammatory response as key determinants of immune escape in preclinical models, and of outcomes from ICB in multiple cancer types (5,8). Given this evidence and the widespread use of non-steroidal and steroidal anti-inflammatory drugs that target the COX-2 pathway for managing inflammation and associated pain, we investigated their effect on the intratumoral immune landscape and potential for enhancing the response to immunotherapy. Using therapeutically relevant regimens and doses, we demonstrated that targeting the COX-2/PGE₂/EP2-4 axis with different types of drugs acutely shifts the tumor microenvironment and enhances the efficacy of immune checkpoint blockade.

The synergistic effect of COX-2/PGE₂/EP2-4 pathway inhibition and ICB treatment was observed irrespective of the different pharmacokinetics and pharmacodynamics of the anti-inflammatory drugs tested. Most surprisingly, corticosteroids, widely considered to be potent immunosuppressants and routinely prescribed to cancer patients for the management of ICB-induced irAEs, also augmented tumor clearance following ICB with either αPD-1 or the combination of αPD-1 plus αCTLA-4. This suggests that corticosteroids, whilst effectively limiting irAEs might simultaneously improve the anti-tumor response. In line with this hypothesis, TNF blockade was recently shown to synergize with αPD-1 therapy whilst concomitantly dampening experimental colitis (40). Our proof-of-concept work supports preclinical evidence that corticosteroids may preserve anti-tumor immunity (30,41,42) and indicates that the use of broad anti-inflammatory drugs could paradoxically boost immune control. While it remains to be determined in which settings this combination may work best, we speculate that corticosteroids may be beneficial where pro-tumorigenic inflammatory responses are prevalent. Furthermore, substantial evidence exists that despite ICB treatment withdrawal, overall survival rates are comparable in immunotherapy patients receiving corticosteroids for pre-existing autoimmune conditions (43,44), or for the treatment of irAEs (16–19). Future studies in both preclinical and clinical settings will be required to reconcile all the conflicting observations and to define the potential conditions in which the use of corticosteroids in patients undergoing ICB might be beneficial or should be contraindicated.

Patient and tumor characteristics such as overall burden, location, oncogenic signaling, presence of tumor-infiltrating T cells, tumor mutational burden, neoantigen clonality and patient microbiome composition, have all been shown to associate with ICB outcome (2,45). Because of this, one of the biggest challenges in the treatment of patients with ICB is the heterogeneous response between patients (1,28). Remarkably, even though our experiments were performed with inbred animals obtained from a single commercial vendor, housed in highly controlled conditions and bearing tumors formed by genetically identical cancer cells, we observed consistent dichotomy in ICB response across models and treatment regimens. This variability in tumor fate post-treatment constitutes a major limitation to the identification of mechanisms underlying efficacy, especially at early timepoints following treatment when responder and non-responder tumors are macroscopically indistinguishable. To overcome this limitation, we utilized a bilateral surgery model in which contralateral tumors showed highly concordant responses following ICB in line with recent reports (31,32). This experimental approach allowed us to classify tumors as responders or non-responders based on the fate of the contralateral tumor.

Unbiased hierarchical k-means clustering of the transcriptome of surgically resected tumors early on treatment identified two major clusters differentially enriched in responding and non-responding mice. The defining feature of the response cluster was the upregulation of an IFN-γ-associated transcriptional program. Accordingly, tumor control following combination treatment with CXB and PD-1 blockade was impaired in mice deficient in IFN-γ or lacking NK, cDC1 or adaptive immunity. This data furhter exposed the importance of an NK/cDC1/T cell axis in ICB-induced tumor immunity (46) and provides additional evidence for IFN-γ as a primary cytokine instructing a rapid response to immune checkpoint inhibitors (11,12, 36). Furthermore, we found that cancer patients benefiting from ICB showed a similar enrichment in the IFN-γ-driven transcriptional response program in both pre- and on-treatment samples.

Remarkably, we showed that this response program could also be rapidly induced by inhibiting PGE₂ production or signaling in as few as four hours. Of note, these changes were transient and wane over time without repeated dosing, revealing a marked plasticity in the tumor immune microenvironment and indicating the need for sustained therapeutic COX-2 inhibition, and potentially for twice and thrice daily dosing, to maximize its potentiating effects on ICB. These findings have direct implications for the design of treatment protocols to combine ICB and inhibitors of the COX-2/PGE₂/EP2-4 axis, which are currently being tested in multiple clinical trials across cancer types (e.g. NCT03155061, NCT03026140).

The relevance of our findings for human malignancies was further demonstrated by showing that addition of CXB altered the inflammatory profile of patient tumors ex vivo using a recently developed PDTF platform. Addition of CXB reduced the production of pro-tumorigenic mediators such as IL6 or CXCL1 while concomitantly enhancing the release of CXCL9 and CXCL10, major IFN-γ-driven CTL-chemoattractants essential for natural and therapy-induced anti-tumor immunity (47). In addition, when combined with anti-CD3 stimulation, CXB further potentiated the activation of CTLs present within human tumor specimens and the production of critical effector cytokines such as IFN-γ or IL-2.

Data from both murine and human tumors highlights the dual role of COX-2 inhibitors in modulating the flavor of inflammation. We have recently shown that monitoring the ratio of these two opposing profiles utilizing the COX-IS constitutes a powerful strategy to predict patient survival and ICB outcome in multiple cancer types (5). Here we found that monotherapy with CXB or EPAT could rapidly lower the COX-IS, ultimately enhancing the efficacy of PD-1 blockade when used in combination. Our efforts to investigate the underpinning mechanistic basis for the synergy between ICB and anti-inflammatory drugs show that while the TME transcriptional profile changed rapidly following treatment, the overall leukocyte infiltrate composition was less affected. Nonetheless, multiparametric immunophenotyping by CyTOF showed that the early molecular remodeling favors the recruitment and expansion of TCF1⁺ naïve-like, cytokine-producing CD8⁺ T cells, whilst limiting their dysfunctional phenotype.

In conclusion, across multiple models we have identified a major role for the COX-2/PGE₂/EP2-4 axis as an independent immune checkpoint that can be therapeutically targeted with widely available drugs. Of major clinical relevance, our work in both mouse and human cancer settings demonstrated that COX-2 inhibition can rapidly remodel the intratumoral immune molecular profile and fuel T cell effector function rather than indiscriminately limiting inflammation. Overall, our findings are consistent with a model whereby anti-inflammatory drugs enhance immune control by limiting COX-2-driven immune evasion and tilting the balance towards cancer inhibitory inflammation. Pharmacological inhibition of the COX-2/PGE₂ axis using readily available anti-inflammatory drugs therefore has great potential to modulate the tumor immune environment and improve the efficacy of existing immune-targeting drugs.

Methods

Cell lines and cell culture

CT26 and 4T1 cells (Cancer Research UK Manchester Institute) are commercially available. The Braf^V600E-driven 5555 melanoma cell line was established from C57BL/6 Braf ^+/LSL-V600E;Tyr::CreERT2 ^+/o;p16 ^INK4a-/- mice (48). COX-2^KO cells (CT26 and 4T1) were generated by CRISPR/Cas9-mediated genome engineering as previously described (5). To restore COX-2 expression in COX-2^KO 4T1 breast cells, the full-length (1.8kb) open reading frame of mouse Ptgs2 was cloned from the parental 5555 melanoma cell line, and subcloned into the retroviral expression vector pFB-neo (Agilent). Following retroviral transduction (see supplementary methods for more details), COX-2^REST cells were selected in presence of 300μg/ml G-418 (Sigma). Knockout and restoration of COX-2 expression were verified by immunoblotting using anti-COX-2 specific antibodies (Cell Signaling, #12282) and by measuring PGE₂ in cell supernatants by ELISA (R&D or Cayman Chemical; Supplementary Fig. S1A and S1B). Cells were maintained at low passage and cultured in standard conditions in RPMI-1640 (Lonza) supplemented with 5% Penicillin/Streptomycin (ThermoFisher Scientific) and 10% Fetal Bovine Serum (Life Technologies) and routinely confirmed to be mycoplasma-free (Venor® GeM gEP Mycoplasma Detection Kit, Minerva Biolabs) and mouse hepatitis virus-free (QIAamp® Viral RNA Mini extraction kit, Qiagen) by qPCR. For inoculation into mice, cells were freshly thawed, grown to 80-90% confluency before passaging and passaged at least 3 times, harvested in the exponential phase of growth by trypsinization (Sigma), washed 3 times with cold PBS (ThermoFisher), filtered through a 70μm filter (ThermoFisher) and resuspended in cold PBS. For in vitro treatment of cells, lyophilized celecoxib (CXB, LC labs) and methylpredinosolone (Sigma) were reconstituted in sterile DMSO. 1x10⁵ CT26 colorectal tumor cells were plated in a 24-well plate, left overnight, then treated with a final concentration of 5μM of CXB or 0.5mg/ml of methylpredinosolone. Both drugs were refreshed after 24h, supernatants harvested the next day and tested for PGE₂ levels.

Animal experiments

For in vivo studies, female and male wild-type (C57BL/6 and BALB/c, Envigo stock #057 and #162) and genetically-modified strains (Ifng ^–/–, Rag ^–/–, Batf3 ^-/- on C57BL/6 background and NSG™ bred at CRUK Manchester Institute) were assigned to experimental groups and housed under specific pathogen-free conditions and in individually ventilated cages in the institutional Biological Research Unit. 1×10⁵ to 5×10⁵ cells were injected subcutaneously in 100μl of PBS into the right flank or bilaterally into both flanks in the surgery model (see also Supplementary methods). No statistical analysis was performed to determine sample size. Most experiments were performed using female mice of 6-10 weeks of age. Tumor size was quantified as the mean of the longest diameter (length) and its perpendicular (width) measured by a hand caliper. Stratified randomization was applied in order to normalize tumor sizes and body weights across treatment groups. The investigators were not blinded to allocation during experiments and outcome assessments. All procedures involving animals were performed under the PDCC31AAF license, in accordance with National Home Office regulations under the Animals (Scientific Procedures) Act 1986. Procedures were approved by the Animal Welfare and Ethical Review Body (AWERB) of the CRUK Manchester Institute and tumor volumes did not exceed the guidelines set by the Committee of the National Cancer Research Institute (49) as stipulated by the AWERB.

In vivo treatments

Lyophilized celecoxib (CXB, LC Labs), EP2 antagonist (TG4-155, Cayman) and EP4 antagonist (ONO-AE3-208, ONO Pharmaceuticals) were weighed using a fine balance and made up in 60:40 ratio of DMSO (1 part, Sigma)/PEG400(5 parts, Sigma):dH2O at a concentration of 3mg/ml (200μl/dose, 30mpk), and administered by oral gavage once or twice daily depending on the experiment (see Fig. legends for details). The dose and regimen for the administration of EPAT to tumor-bearing mice were chosen based on dose-escalating pharmacokinetic studies monitoring the concentration of EP2 and EP4 antagonists in plasma. Lyophilized methylprednisolone (MP, Solu-Medrone, Pfizer UK) was weighed and made up in PBS at a concentration of 1mg/5ml (1mpk, 20μg/dose) or 100mg/5ml (100mpk, 200μg/dose) and injected intra-peritoneally in 100μl for 2 to 5 days depending on the experiment. After 5 days of treatment with methylprednisolone, prednisolone oral solution (Logixx Pharma Solutions Ltd) was diluted in PBS to a concentration of 0.1mg/ml and mice dosed with 200μl per os (1mpk, 20μg/dose) for up to 16 days. Mice were injected with 200μg of αPD-1 (RMP1-14, BioXCell) alone or in combination with 100μg αCTLA-4 (9D9, BioXCell) twice weekly for up to 6 doses. An alternative αPD-1 clone (4H2, Ono Pharmaceutical) was also tested alone or in combination with EPAT achieving similar results as with the BioXcell clone. For NK cell depletion, mice were injected i.p. with a single dose of 200μg of anti-NK1.1 (PK136, BioXcell, #BE0036) and 50μl anti-ASIALO-GM-1 (Biolegend, #146002) either one day before treatment for transcriptomic analysis, or one day before inoculation of cells, followed by bi-weekly treatment for tumor growth studies.

RNA isolation and Quantitative PCR (qPCR)

Tumors were collected in PureZOL Reagent (BioRad) and stored at -80°C. For processing, tumors were dissociated with 5mm Stainless steel beads (Qiagen) using the TissueLyser II (QIAGEN). Total RNA was extracted using the Direct-zol RNA Mini Prep Kit (Zymo Research) following the manufacturer’s recommendations and included a DNAse digestion step. RNA was quantified using a NanoDrop One (ThermoFisher) or a Bioanalyzer (Agilent Technologies) for RNA-seq. For qPCR, cDNA was synthesized using 1-2μg of total RNA by reverse transcription using High Capacity cDNA archive kit (Applied Biosystems) and included an RNAse inhibitor (Promega). Quantitative real-time PCR was performed using TaqMan probes (Applied Biosystems) and either TaqMan Universal PCR MasterMix when ran on the QS5 fast real-time PCRsystem (Applied Biosystems) or consumables from the 96.96 Dynamic array™ when ran on the Biomark® HD system (FLUIDIGM). Data were analyzed with the Δ2CT method (Applied Biosystems, Real-Time PCR Applications Guide). Z-score normalization was performed to pool experiments and cumulative z-scores were calculated by adding z-scores of multiple markers per individual mouse. The COX-IS ratio was calculated as previously described (5).

RNA sequencing and analysis in mouse tumors

For bulk RNA-sequencing, mRNA libraries were prepared using Lexogen QuantSeq 3′-mRNAseq Library Prep Kit (Illumina) from 500ng total RNA, quantified by Bioanalyzer, and sequenced on the Illumina NextSeq500. RNA-Seq reads were quality checked using FastQC v.2.17.1.14 (www.bioinformatics.babraham.ac.uk/projects/fastqc/), trimmed using Trim Galore.v. 0.6.5 and aligned in single-end mode to the mouse genome assembly (GRCm38.75) using the STAR aligner v.2.6.1d with default parameters. Mapped data were converted to gene level integer read counts (expression) using feature Counts and Ensemble GTF annotation (Mus_musculus.GRCm38.75.gtf). Feature Counts were preprocessed (minimum CPM of 0.5 in at least 3 samples) and normalized using the ‘voom’ model within the limma package of Bioconductor. Unsupervised clustering analysis by non-negative matrix factorization (NMF consensus, www.genepattern.org) method was performed to identify the presence of potential transcriptional programs associated with outcome/treatment. Enrichment of molecular pathways (MSigDB) was evaluated by GSEA using the GenePattern Analytical Toolkit (www.genepattern.org). Differential gene expression analysis was performed using comparative marker selection method (Gene Pattern modules, www.genepattern.org) and DESeq2 package from Bioconductor. DEGs were defined based on linear fold change ± 1.5 and p-value/false discovery rate <0.05. The resulting gene list was analyzed with IPA software. Estimation of cell types in the tumor microenvironment was performed using the MCP-counter (33) and Consensus^TME (34) methods to determine the relative abundance of 10 cell types and a general immune score of the total level of immune cell infiltration in each tumor sample, respectively.

Transcriptomic analysis in human patient datasets

Details of publicly available RNA sequencing datasets of cancer patients receiving ICB can be found in Supplementary Table S4. When applicable RNA sequencing reads were processed and mapped as described above using the human genome assembly (Homo sapiens.GRCh38) for mapping and annotation. Processing and normalization of raw feature count matrixes were performed using the edgeR package (version 3.24.3). Genes were filtered out based on a threshold of 0.25 CPM in 10% samples. A Log2 CPM+1 expression matrix was generated and used for downstream analysis. Alternatively, if available, normalized FPKM and TPM values were obtained from GEO database. For AIR enrichment analysis by GSEA, partial or complete responses were pooled as responders, and progressive disease and stable disease (SD) as non-responders.

FACS analysis

For analysis of tumor-infiltrating leukocytes, tumors were collected into ice-cold RPMI on ice. The surface of the tumor samples was dried with paper and tumor weight recorded. Samples were transferred into C-tubes (Miltenyi Biotech) containing RPMI and Collagenase IV (200U/ml, Worthington Biochemical) and DNase I (0.2mg/ml, Roche) then minced using scissors. The C-tubes were placed in a GentleMACS Octo Dissociator (Miltenyi Biotech) and tumors disaggregated with 2 rounds of the automated program m_impTumor_02_01. Dissociated tumors were incubated for 35’ at 37°C and disaggregated for one more round. The C-tubes were centrifuged and pellets resuspended in cold RPMI before being filtered through a 70μm cell strainer and pelleted. Cell suspensions were resuspended in PBS for CyTOF or FACS buffer (PBS containing 2% FCS, 2 mM EDTA and 0.01% sodium azide) for flow cytometry analysis. Fc receptors were blocked with anti-CD16/32 (2.4G2, eBioscience) 5’ before the staining. Cell viability was determined by Aqua LIVE/Dead-405 nm staining (Invitrogen). Tumors were stained with combinations of the following antibodies: CD45-BV605 (30-F11), CD11b-BV785 (M1/70), Ly6G-PE-CF594 (1A8), Ly6C-FITC (AL-21), F4/80-PE-Cy7 (CI: A3-1) anti-MHCII I-A/I-E APC-eFluor780 (114.15.2), anti-CD11c-PerCP/Cy5.5 (N418), anti-CD103 PE (2E7), NK1.1-APC (PK136), CD49b-APC (DX5), XCR1-BV421 (ZET), CD3ε PercCP-Cy5.5 (145-2C11), CD8α-PE (53-6.7), CD4-FITC (RM4-5), CD44-APC-eFluor780 (IM7), IFNγ-eFluor450 (XMG1.2) from eBioscience or BioLegend. For intracellular cytokine detection, cells were stimulated ex vivo for 4h with Cell Stimulation Cocktail (ThermoFisher) and stained using the Intracellular Fixation & Permeabilization Buffer Set (eBioscience) following manufacturer instructions. Monensin (eBiolegend) and Brefeldin A (eBiolegend) solutions were added 2h before the staining and non-specific binding of intracellular epitopes was blocked by pre-incubation of cells with 2% normal rat serum (ThermoFisher). Live cell counts were calculated from the acquisition of a fixed number (5000) of 10μm latex beads (Beckman Coulter) mixed with a known volume of cell suspension. Spectral overlap was calculated using live cells or VersaComp antibody capture beads (Beckman Coulter). Cells were acquired on a Fortessa X-20 (BD Bioscience) or on a Novocyte (ACEA). Flow cytometry standard .fcs files were analyzed using FlowJo v10.6.2 (Tree Star Inc.).

Multiplexed immunofluorescence

Multiplexed Tyramide Signal Amplification (TSA) immunofluorescence staining was performed using the BOND RX automated platform (Leica Microsystems). 4μm sections of FFPE tumors were cut and mounted on charged slides. Dewaxing and heat induced epitope retrieval (HIER) of slides was automated on the Bond RX using epitope solution 1 (AR9961) for 20’ at 100°C. Using the Open Research Kit (DS9777), endogenous peroxidase was blocked using 3% hydrogen peroxide (VWR) for 10’ and the slides further blocked with 10 % w/v casein (Vector SP5020 in TBST). Antibody application, detection and TSA amplification was conducted in sequential rounds following the same general procedure: Incubation with the primary antibody in Bond antibody diluent (AR9352) for 30’ (in the following sequence: CD8 5μg/ml (eBioscience, 14-0808), CD4 5μg/ml (eBioscience, 14-9766) and Foxp3 2.5μg/ml (eBioscience, 14-5773-82), followed by detection using anti-rat ImmPRESS HRP (Vector MP5444) (RTU) for 30’, followed by premixed TSA reagent (Perkin Elmer) 1/200 for 10’. Antibody sequence and TSA-fluorophore selection were optimised to reduce non-specific staining and tyramide binding site competition. Following labelling with TSA, each antibody was removed using a heat stripping step (epitope solution 1 (AR9961) for 10’ at 100°). Finally, nuclei were counterstained with dapi (Thermo Fisher, 62248) for 15’ (0.33μg/ml) and mounted in coverslips with ProLong Gold antifade mountant (Thermo Fisher, P36930). Images were scanned at 20X on an Aperio VERSA (Leica Biosystems), then analyzed and quantified using the HALO® (Indica Labs) Highlex FL module.

Analysis of patient-derived tumor fragments (PDTF)

PDTF: Patient characteristics and tumor sample processing

Tumor samples were collected from individuals with melanoma, non-small cell lung cancer, ovarian cancer, colorectal or renal cell carcinoma undergoing surgical treatment between September 2017 and August 2020 at the Netherlands Cancer Institute (NKI-AVL), The Netherlands. The study was approved by the NKI-AVL review board and performed in compliance with all relevant ethical regulations. Patient characteristics are provided in Supplementary Table S5. All studies were performed in compliance with ethical regulations and patients consented to the research usage of material not required for diagnostic either by opt-out procedure or upon prior written informed consent (after May 23, 2018). Tumor sample processing and storage was done as described (37). Briefly, solid tumor lesions were identified by a pathologist. One part of the tumor was embedded in paraffin for histological analysis, while the other part of the tissue was processed by manual cutting into small tumor fragments of 1-2 mm³ size on ice. Subsequently, PDTFs from different areas were mixed and frozen in cryovials containing 1ml of freezing medium (FBS with 10% DMSO, Sigma) with 8-15 PDTFs per vial. The vials were cryopreserved in liquid nitrogen until further usage.

PDTF: ex vivo cultures

PDTF cultures were performed as described (37). In brief, PDTFs were slowly thawed and extensively washed in medium (DMEM, Thermo Fisher) supplemented with 1mM sodium pyruvate (Sigma), 1x MEM non-essential AA (Sigma), 2mM L-glutamine (Thermo Fisher), 10% FBS (Sigma) and 1% Penicillin-Streptomycin (Roche). Subsequently, PDTFs were embedded in an artificial extracellular matrix as follows: firstly, collagen I (Corning; 1 mg/ml final concentration), sodium bicarbonate (Sigma, 1.1% final concentration), and tumor medium were combined on ice, then slowly added to ice-cold matrigel (Matrix High Concentration, Phenol Red-Free, BD Biosciences, 4mg/ml final concentration) to form the final matrix. After preparation of the matrix on ice, a 96-well flat-bottom plate was coated with a bottom layer of 40μl matrix, which was solidified by incubating for 30’ at 37°C. One PDTF per well was placed on top of the matrix, followed by addition of a second layer of 40μl matrix. After subsequent solidification at 37°C for 30’, 120μl tumor medium was added to each well. For stimulated conditions, the medium was supplemented with either anti-CD3 (OKT3, Biolegend) at a final concentration of 2μg/ml, celecoxib (CXB, LC labs) at a final concentration of 5μM or a combination of anti-CD3 and CXB. PDTFs were incubated for 48hrs at 37°C before readout.

PDTF: flow cytometry analysis

PDTFs were analyzed by flow cytometry using the following antibodies: anti-CD45 PerCP Cy5.5 (2D1), from Invitrogen; anti-CD8 BUV563 (RPA-T8), -PD-1 PE-Cy7 (EH12.1), -CD137 PE (4B4-1), all from BD Biosciences; anti-CD3 FITC (SK7), -CD8 BV605 (RPA-T8), -CD4 BV421 (SK3), -CD19 BV605 (SJ25C1), -FoxP3 AF647 (259D), -CD11c PE (Bu15), -CD16 Alexa 700 (3G8): -OX40 APC (BerACT35) and -CD25 AF700 (BC96), all from Biolegend. For analysis of immune cell composition, either PDTFs were thawed as described above or cultured PDTFs retrieved from the matrix and pooled for each experimental condition. Next, the PDTFs were processed into single-cell suspensions by enzymatic digestion, washed and filtered over a 150μm filter mesh. Cells were incubated with Fc receptor blocking agent (eBioscience) and Zombie UV (Biolegend) or Live/Dead Near-IR Dye (Thermo Fisher) for 20’ at 4°C. Cells were subsequently washed and resuspended in 50μl of staining buffer containing the above-described antibodies, and incubated for 20’ at 4°C. Cells were then washed, fixed and permeabilized using Fix/Perm solution (eBioscience) for 30’ at RT. After washing, cells were resuspended in Permeabilization Buffer (eBioscience) containing intracellular antibodies and incubated for 40’ at RT. Lastly, cells were washed before data acquisition. Data acquisition was performed using a BD LSR II SORP or a Fortessa SORP cell analyzer (BD Biosciences). Data was collected using the BD FACS Diva Software version 8.0.1 (LSR II SORP) and version 8.0.2 (Fortessa SORP) and analyzed with FlowJo v10.6.1 (Tree Star Inc.).

PDTF: Assessment of soluble mediators and PGE₂ levels

Culture supernatant was taken after 48h from PDTF single wells, stored at -80°C and thawed for assessment of cytokines, chemokines or PGE₂ levels. Presence of indicated cytokines and chemokines was detected using the LEGENDplex™ Human Th Cytokine and Human Proinflammatory Chemokine panels (both from Biolegend). Levels of PGE₂ in supernatants were determined as described above.

Mass Cytometry methods

Mass cytometry: antibodies and antibody conjugation

Antibody information is listed in Supplementary Table S6. Where indicated antibodies were purchased pre-conjugated (Fluidigm). In-house conjugations were performed using Maxpar X8 Antibody Conjugation Kits (Fluidigm), with the addition of an equal volume of PBS-based Antibody Stabilization Buffer (Candor Biosciences, 13150) containing 0.6mg/ml sodium azide (Sigma Aldrich, S8032). To generate cisplatin conjugates, 200μg of antibody was reduced as in the method above and incubated with 200μl of 400μM monoisotopic cisplatin (BuyIsotope, custom order) in C-buffer from the Antibody Conjugation Kits at 37°C for 90’ and washed and stored as for the polymer/lanthanide conjugates. Antibodies were titrated in panels by staining samples of known positive and negative controls.

Mass cytometry: live/dead and extracellular staining

To label cells in S-phase, mice were injected with 10mg/ml of 5-iodo-2’-deoxyuridine (IdU) (Sigma Aldrich, 17125) prepared in a minimally basic solution of 0.01M sodium hydroxide (NaOH) (Sigma Aldrich, 757527) in water, 2h before the mouse was culled by Schedule 1 method and tissues collected. Tumors were dissociated as described above. Live cells were spun at 300g for 6’ and fixed cells spun at 1000g for 6’. The disaggregated tumor cell pellet was resuspended in 300μl of ice-cold PBS, vortexed well and 300μl of 1μM 198Pt monoisotopic cisplatin (Fluidigm, 201198) in PBS added, followed by vortexing. After 1’ incubation, the staining was quenched with 20ml of CSM-E (Cell Staining Buffer - Extracellular) consisting of 5mg/ml Bovine Serum Albumin (BSA) (Sigma Aldrich, A3294), 0.5% v/v FBS (Thermo Fisher) and 0.2mg/ml DNAse1 in PBS. The cells were counted and 3x10⁶ cells were aliquoted into a 5ml polypropylene FACS tube, washed with 3ml CSM-E and pelleted. The cells were incubated in 20μl of 100U/ml heparin sodium salt (Sigma Aldrich, H3393) in PBS for 5’ on ice, followed by metal-conjugated anti-CD64 antibody for 10’ on ice, followed by unconjugated anti-CD16/32 antibody for 5’ on ice, before adding the remaining master mix of extracellular antibodies in 50μl CSM-E (see Supplementary Table 6). After 45’ on ice, the cells were washed twice with 4ml of CSM-E and fixed/permed using FOXP3 Fixation/Permeabilization Kit (Thermo Fisher) following manufacturer’s instructions. After permeabilization the cell pellet was resuspended in 1ml of 10% v/v DMSO (Sigma Aldrich) in CSM-I (Cell Staining Buffer-Intracellular), consisting of 5 mg/ml BSA and 0.2mg/ml sodium azide in PBS, vortexed and frozen at -80°C.

Mass cytometry: barcoding, pooling and intracellular staining

Cells were thawed at RT and washed with 4ml PBS. The pellet for each sample was barcoded using the Cell-ID 20-plex Pd Barcoding Kit (Fluidigm, 201060) following manufacturer’s instructions, and washing twice with CSM-I at the end. Samples were pooled in FOXP3 Permeabilization Buffer and pelleted. For each sample included in the pooled sample, 10μl of 100U/mL heparin sodium salt in PBS and 0.5μl of Fc block was added and the sample mixed by gently rocking. After incubating for 5’ at RT in the dark, a master mix of intracellular targeting, metal-conjugated antibodies (see Supplementary Table S6) in CSM-I was added. For each sample included in the pooled sample, one equivalent of antibody and 25μl of CSM-I was used. After 45’ of incubation, the cells were washed twice with 4ml of CSM-I fixed in 4% Paraformaldehyde (PFA) (Thermo Fisher). The sample was vortexed and stored overnight at 4°C.

Mass cytometry: DNA staining and acquisition

On the day of acquisition, 0.5μl of 125μM of Cell-ID Iridium Intercalator (Fluidigm, 201192A) for each individual sample included in the pooled sample was added to the cells/PFA mixture and vortexed. After 1h of incubation at RT the cells were washed once with CSM-I aliquoted and kept on ice until ready to run each tube. Each cell pellet was washed twice with water and resuspended at a concentration of 1x10⁶ cells/ml in 15% EQ Four Element Calibration Beads (Fluidigm, 201078) in water, filtered twice through 70μm Filcons (BD Biosciences, 340633) and acquired on a Helios Mass Cytometer (Fluidigm), using a Super Sampler (Victorian Airship & Scientific Apparatus LLC) at a maximum of 500 events/s.

Mass cytometry: data processing & analysis

FCS files were normalized for signal-drift during the acquisition run using the in-built Helios normalization tool (Fluidigm) and individual sample events deconvoluted using the stand-alone debarcoder (50), using a Mahalanobis distance of 15 and a minimum barcode separation of 0.26. Individual sample FCS files were uploaded to Cytobank (www.cytobank.org, Beckman Coulter). As per standard methods, live cell events were selected based on 191Ir positivity and 198Pt negativity. 191Ir+ debris and cell doublets and aggregates were removed based on event length. If possible, target cells were selected by manual biaxial gating: T cell events selected as CD45⁺CD3ε⁺, further divided in CD8⁺ and CD4⁺ T cells. Target cells were exported as FCS files and uploaded to the Cytofkit2 package (version 2.0.1). Cells were clustered using FlowSOM and visualized using UMAP projections and expression overlays, exporting cell data with annotated clusters for further downstream analysis. Plotting and statistical analysis was done using R Statistical software and the result files exported from Cytofkit2. Violin plots and expression plots were generated using the ggplot2 package in R with expression data transformed by cytofAsinh method. For cross-cluster phenotype comparison between experimental groups, the FCS files exported from Cytofkit2 including the cluster annotation were loaded in Cytobank and the cells with the phenotypes of interest where gated manually (i.e. positive cells for a specific marker or S-phase cells as IdU⁺ Ki67⁺). The percentage of cells with the phenotype of interest as well as the median mass intensity (MMI) were calculated for each cluster. Comparisons were performed using Kruskal-Wallis test and Dunn’s test for pairwise comparisons with Holm-adjusted p-values.

Statistical analysis

Graphs were plotted using GraphPad Prism v8.4.1 (GraphPad Software Inc.) and R software (R project). Statistics were calculated with GraphPad Prism and values expressed as mean ± SEM of biological replicates. Data were analyzed with the following tests (see Fig. legends for details): Unpaired Student’s t-test, Mann–Whitney U-tests in the case of non-Gaussian distributed data; One-way ANOVA tests adjusted for multiple comparisons using the Kruskal-Wallis test, Log-rank (Mantel-Cox) for the analysis of Kaplan-Meier survival curves. A p value < 0.05 (*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001) was considered significant.

Statement of significance.

Through performing in-depth profiling of mice and human tumors, this study identifies mechanisms by which anti-inflammatory drugs rapidly alter the tumor immune landscape to enhance tumor immunogenicity and responses to immune checkpoint inhibitors.

Data availability

RNA sequencing data have been deposited in the NCBI’s Gene Expression Omnibus database and can be accessed through the GEO Series GSE160789. The accession number for bulk tumor transcriptomes of surgically excised CT26 colorectal tumors treated with αPD-1 and/or CXB is SubSeries GSE160785. The accession number for bulk tumor transcriptomes of 5555 melanoma tumors treated with vehicle, αPD-1 and/or EPAT is SubSeries GSE160788.