Targeting ire1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancer

Targeting ire1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancer

Play all audios:

Loading...

ABSTRACT Unfolded protein response (UPR) is a central stress response pathway that is hijacked by tumor cells for their survival. Here, we find that IRE1α signaling, one of the canonical UPR


arms, is increased in prostate cancer (PCa) patient tumors. Genetic or small molecule inhibition of IRE1α in syngeneic mouse PCa models and an orthotopic model decreases tumor growth. IRE1α


ablation in cancer cells potentiates interferon responses and activates immune system related pathways in the tumor microenvironment (TME). Single-cell RNA-sequencing analysis reveals that


targeting IRE1α in cancer cells reduces tumor-associated macrophage abundance. Consistently, the small molecule IRE1α inhibitor MKC8866, currently in clinical trials, reprograms the TME and


enhances anti-PD-1 therapy. Our findings show that IRE1α signaling not only promotes cancer cell growth and survival but also interferes with anti-tumor immunity in the TME. Thus, targeting


IRE1α can be a promising approach for improving anti-PD-1 immunotherapy in PCa. SIMILAR CONTENT BEING VIEWED BY OTHERS EZH2 INHIBITION ACTIVATES A DSRNA–STING–INTERFERON STRESS AXIS THAT


POTENTIATES RESPONSE TO PD-1 CHECKPOINT BLOCKADE IN PROSTATE CANCER Article 22 March 2021 MODULATING THE UNFOLDED PROTEIN RESPONSE WITH ONC201 TO IMPACT ON RADIATION RESPONSE IN PROSTATE


CANCER CELLS Article Open access 19 February 2021 AUTOPHAGY INHIBITION BY TARGETING PIKFYVE POTENTIATES RESPONSE TO IMMUNE CHECKPOINT BLOCKADE IN PROSTATE CANCER Article 02 August 2021


INTRODUCTION The endoplasmic reticulum (ER) is the largest organelle in the cell. It is the major site for proper protein folding, processing and trafficking of membrane-bound and secreted


proteins. In addition, ER is the major site for Ca2+ storage and functions as a stress sensor responding to increased demand for protein production and folding under both physiological and


pathological conditions. ER stress initiates a series of coping mechanisms that are termed the unfolded protein response (UPR), which restructures the cellular transcriptional,


translational, and degradation pathways to help resolve the defects in protein folding1,2. This is achieved through the activation of three trans-membrane ER proteins: inositol requiring


enzyme 1 alpha (IRE1α), protein kinase RNA-dependent-like ER kinase (PERK) and activating transcription factor 6 (ATF6). IRE1α is both a protein kinase and an atypical site-specific RNase.


Upon activation, IRE1α RNase function activates the translation of transcription factor X-box protein 1 spliced (XBP1s) by removing an intron from the XBP1 mRNA; in addition, it depletes


select mRNAs through the process of regulated IRE1α-dependent decay (RIDD)1,2. XBP1s is involved in gene expression that supports ER-mediated protein folding, as well as ER-associated


degradation (ERAD) of misfolded proteins, whereas RIDD depletes specific ER-targeted mRNAs to relieve the load on ER, as well as proteins involved in other processes in the cell. Recent


studies have implicated IRE1α-XBP1s pathway deregulation in a number of pathological conditions, including metabolic disease, neurodegenerative disease, and cancer1,2,3. We have previously


found that IRE1α-XBP1s signaling is significantly increased in prostate cancer (PCa)4,5. Consistently, genetic or small molecule targeting of IRE1α resulted in significant inhibition of PCa


tumor growth in multiple xenograft models in immunodeficient mice. We have shown that this was due, at least in part, to the inhibition of c-MYC expression driven by IRE1α-XBP1s signaling5.


IRE1α-XBP1s signaling has also been implicated in immune cell function (for a review, see ref. 6). In bone-marrow-derived macrophages, Toll-like receptor 2 (TLR2) and TLR4 activated


IRE1α-XBP1s signaling to enable production and secretion of cytokines, such as tumor necrosis factor (TNF), interleukin 1 beta (IL-1β) and IL-67. In TLR8-activated mouse bone marrow-derived


dendritic cells (BMDCs), IRE1α–XBP1s signaling increased IL-23 expression upon palmitate exposure8. In dendritic cells (DCs), macrophages and neutrophils that are exposed to ER stress or


stimulated by plasma membrane-bound TLRs, IRE1α-XBP1s signaling was necessary for normal eicosanoid metabolism and production of the pain-causing lipid mediator prostaglandin E2 (PGE2)9. In


natural killer (NK) cells, IRE1α-XBP1s signaling was required for response to viral infection and tumors10. In addition to these and other roles of the IRE1α-XBP1s pathway in normal immune


function, recent studies have demonstrated that it is also involved in antitumor immunity via altering the function of myeloid cells, T cells, and NK cells in the tumor microenvironment


(TME) (for a review, see ref. 10). For example, IRE1α–XBP1s signaling was found to be essential for proliferation of NK cells in mouse melanoma models11. In metastatic ovarian cancer mouse


models, there is sustained IRE1α–XBP1 activation in tumor-associated DCs driving triglyceride biosynthesis and lipid droplet formation, resulting in the loss of their antigen-presenting


capacity12. Similarly, T cells isolated from human ovarian cancer specimens, either solid tumors or ascites fluid, had increased IRE1α-XBP1s pathway activity, and this was linked to


decreased intratumoral T cell infiltration and reduced interferon-γ (IFN-γ) expression13. These findings suggest that inhibiting the IRE1α-XBP1s pathway may improve the efficacy of immune


checkpoint blockade (ICB) and adoptive T cell immunotherapies in solid tumors that are refractory to these approaches. Here, we study the potential effects of IRE1α signaling on the PCa TME


in multiple syngeneic mouse models as well as in an orthotopic model. We demonstrate that IRE1α signaling in cancer cells reshapes the TME and pharmacological targeting of IRE1α can enhance


the response to anti-PD-1 blockade therapy. These findings have clinical implications for treating immunologically ‘cold’ PCa. RESULTS IRE1Α IS UPREGULATED IN HUMAN PCA SPECIMENS We have


previously shown that androgens, which activate a major signaling pathway implicated in PCa progression, modulate IRE1α-XBP1s branch of the UPR4. Consistently, inhibition of IRE1α RNase


activity or knockdown of XBP1s strongly interfered with PCa in multiple preclinical models5. To further delineate the role of IRE1α in PCa, we first assessed IRE1α expression in human PCa


specimens compared with normal prostate using immunohistochemistry (IHC). IRE1α expression was significantly increased in PCa samples compared to normal prostate tissue and correlated with a


higher Gleason score (Fig. 1A–C), suggesting that it may have prognostic significance. This observation was further confirmed in an independent PCa cohort where IRE1α expression was also


significantly increased in PCa samples compared to adjacent normal tissues (Fig. 1D). In addition, in the Cancer Genome Atlas (TCGA) dataset, IRE1α mRNA expression is highly correlated with


androgen receptor (AR) mRNA and protein levels (Supplementary Fig. 1A, B). This was consistent with the IHC data where there were increased nuclear AR levels with increasing IRE1α expression


(Supplementary Fig. 1C, D). Further analysis of the TCGA dataset, particularly the RPPA (Reverse Phase Protein Array) data, suggests that the phosphorylation levels of AKT, mTOR, and ERK


are associated with increased IRE1α mRNA expression (Supplementary Fig. 1E, F). In addition, IRE1α mRNA levels correlated with the hypoxia-inducible factor 1 subunit alpha (HIF1A) mRNA


expression (Supplementary Fig. 1G), suggesting that there may be an interplay between IRE1α signaling and cellular hypoxic responses in PCa. Given the central role of IRE1α in UPR signaling,


we next evaluated UPR activity across different PCa disease stages using the hallmark UPR gene signature in publicly available datasets14. In contrast to normal prostate samples, the UPR


score was significantly increased in both tumor-adjacent normal samples and primary PCa; furthermore, primary PCa samples displayed higher scores than tumor-adjacent normal samples,


suggesting the induction of UPR in the TME (Fig. 1E). To further explore whether this difference in UPR signature expression involves the IRE1α-XBP1s branch of UPR, we determined XBP1s mRNA


levels in the TCGA PCa dataset15 by dividing the patients according to XBP1s levels (top and bottom 40% expression) (Fig. 1F). Gene set enrichment analysis (GSEA) showed that genes


regulating hallmark UPR and protein secretion were downregulated in patients with low XBP1s read counts, as expected (Fig. 1G). Consistent with our previous findings5, PCa patient tumors


exhibiting high XBP1s mRNA levels showed increased expression of genes that regulate hallmarks of cancer growth such as androgen response, cancer progression and transformation, mTORC1


signaling, oxidative phosphorylation and MYC targets (Fig. 1H). These findings further demonstrate the significance of IRE1α-XBP1s signaling in PCa. TUMOR-INTRINSIC IRE1Α LOSS MAY INFLUENCE


ANTI-TUMOR IMMUNITY IN THE PCA TME Activation of the IRE1α-XBP1s branch of the UPR has been associated with cancer growth and malignancy16. However, the impact of IRE1α activation in cancer


cells on anti-tumor immune responses remains largely unknown. To investigate this possibility, we used the Myc-CaP syngeneic mouse PCa model, derived from a transgenic mouse strain in which


human c-MYC oncoprotein expression is specifically targeted to the prostate17,18. We first generated IRE1α knock-out (KO) Myc-CaP cell lines using CRISPR-Cas9 mediated genome editing


(Supplementary Fig. 2A, B). Interestingly, IRE1α KO Myc-CaP cells did not display any changes in cell viability and growth in vitro either at basal levels (Supplementary Fig. 2C, D) or upon


treatment with the UPR activator thapsigargin (TG) (Supplementary Fig. 2E); this is in contrast to its key roles in cancer, including PCa (for reviews, see refs. 16,19). In addition, genetic


or pharmacological targeting of IRE1α in Myc-CaP cells in vitro did not markedly affect apoptosis, either in the absence or presence of TG (Supplementary Fig. 2F-H). Furthermore, the cell


cycle was not consistently affected by the absence of IRE1α, although two of the three clones showed slight increases in G2 phase (Supplementary Fig. 2I). To gain insight into the global


transcriptomic changes upon loss of IRE1α, we performed RNA-seq, which revealed a strong correlation (_r_ > 0.56) among the three different IRE1α KO clones, suggesting a high degree of


similarity in their gene expression profiles (Supplementary Fig. 3A). Analysis of significantly deregulated genes identified 240 genes that were consistently downregulated across all three


independent IRE1α KO clones (Supplementary Fig. 3B) (Supplementary Data 1). As expected, GSEA of these genes demonstrated enrichment of UPR and UPR-related pathways, including


IRE1α-activated chaperones, protein processing in the ER, and asparagine N-linked glycosylation (Supplementary Fig. 3C). In addition, hallmark UPR gene expression was significantly


downregulated in IRE1α KO cells (Supplementary Fig. 3D). These data are consistent with known pathway connections of IRE1α signaling1,2. We next investigated the functional role of


IRE1α-XBP1s signaling in the PCa TME. To explore the tumor cell-intrinsic effect of IRE1α activation on PCa tumor growth, we injected the IRE1α WT and the three KO clones of Myc-CaP cells


into FVB mice and monitored tumor growth. Loss of IRE1α dramatically reduced tumor growth (Fig. 2A, B). Consistent with these results, the growth of IRE1α KO Myc-CaP tumors was significantly


hindered in the orthotopic setting compared to wild type Myc-CaP tumors (Supplementary Fig. 3E, F). To evaluate whether the reduction in tumor growth was attributable to the IRE1α-XBP1s


signaling axis, we ectopically expressed XBP1s in Myc-CaP IRE1α KO #1 cells, which showed the greatest reduction in tumor growth. XBP1s overexpression in IRE1α KO cells rescued the


expression of IRE1α-XBP1s target genes (Edem1, p58IPK, and Pdia6) in vitro indicating its functionality (Supplementary Fig. 3G). Importantly, ectopic XBP1s expression rescued the growth of


IRE1α KO tumors in vivo (Supplementary Fig. 3H). To evaluate whether the decreases in IRE1α KO Myc-CaP tumor growth were due to protective anti-tumor immunity, we conducted a xenograft


experiment in nude mice that had a greatly reduced number of T lymphocytes. As opposed to dramatic regression in tumor growth observed in immunocompetent FVB mice, loss of IRE1α in Myc-CaP


cells in nude mice only mildly affected tumor growth at later time points (Fig. 2C). These data suggest that anti-tumor immunity may play a role in mediating the IRE1α effects in tumor


growth in FVB mice. To explore the potential mechanisms of tumor regression upon IRE1α targeting, we performed RNA-seq from IRE1α WT and the three independent KO tumors. GSEA on the RNA-seq


data confirmed the downregulation of UPR target genes (Supplementary Fig. 4A) and classical IRE1α target pathways, such as the expression of chaperones and proteins involved in N-linked


glycosylation (Fig. 2D) (Supplementary Data 1). Consistent with human PCa patient tumors that exhibit low XBP1s and IRE1α mRNA levels (Fig. 1H and Supplementary Fig. 1E–G), there was


downregulation of genes that control hallmarks of cancer growth in IRE1α KO tumors such as hypoxia, oxidative phosphorylation, and mTORC1 signaling (Supplementary Fig. 4B–E). Interestingly,


the loss of IRE1α also significantly increased immune-related gene expression in the tumors (Fig. 2E). Among the top enriched pathways for upregulated genes were IFN response-related


pathways followed by various aspects of immune function, such as innate and adaptive immunity, T and NK cell-mediated immunity and their activation (Fig. 2E–G). Altogether, these results


indicated that IRE1α-XBP1s signaling in cancer cells may alter protective immunity in the PCa TME. IRE1Α LOSS IN CANCER CELLS AUGMENTS IFN-Γ SIGNALING RESPONSE IN THE PCA TME It is


noteworthy that while hallmark UPR genes were downregulated in IRE1α-deficient tumors, there was a significant increase in the expression of the genes involved in IFN- α or -γ


transcriptional responses (Fig. 3A, B). The roles of IFN-γ in anti-tumor immune responses, particularly in the context of T and NK cell-mediated immunity, as well as antigen presentation,


are well established20,21. Although IFN-γ transcriptional response genes were upregulated in IRE1α KO tumors in vivo, their expression was not changed in KO cells in vitro (Fig. 3C). Altered


expression of ER stress and IFN-γ response genes in IRE1 KO tumors were validated by qPCR analysis (Fig. 3D). To obtain additional insight into the characteristics of Myc-CaP tumor TME upon


IRE1α loss, we performed proteomics analysis on tumor samples from IRE1α KO clones #2 and #3 (no tumor material was left for this analysis for clone #1). We detected 6505 proteins in IRE1α


WT and KO tumors (Supplementary Data 2). Among these, 202 were upregulated and 378 were downregulated (absolute fold-change ≥ 1.2 and p < 0.05, WT vs KO) (Supplementary Data 2). Analysis


of these data revealed that proteins modulating the immune system, complement system, and notably IFN-α and -γ responses, were significantly upregulated and enriched in the TME upon loss of


IRE1α (Fig. 3E, F). In contrast, proteins regulating hallmarks of cancer growth and survival, such as UPR, mTORC1 signaling, MYC targets, glycolysis, oxidative phosphorylation, and hypoxia


were all significantly enriched among the downregulated proteins in IRE1α KO tumors, consistent with the RNA-seq data (Fig. 3F and Supplementary Fig. 4). Coordinate analysis of


differentially regulated genes from both RNA-seq and proteomics data revealed 182 upregulated and 192 downregulated common genes (Fig. 3G and Supplementary Data 2) that displayed significant


changes for IFN-γ response (e.g. HELZ2, ZBP1, PML, etc.) and UPR target gene expression (e.g. DNAJC3, MTHFD2, PDIA6, etc.), respectively (Fig. 3H). These findings suggest that loss of IRE1α


in Myc-CaP cells enhances the IFN transcriptional response within the TME, which is essential for anti-tumor immunity. LOSS OF IRE1Α IN CANCER CELLS REPROGRAMS THE PCA TME LANDSCAPE To


explore the molecular and cellular mechanisms that underlie the enhanced IFN response in IRE1α KO tumors, we conducted a new experiment where WT and IRE1α KO Myc-CaP tumors were subjected to


single-cell RNA-sequencing (scRNA-seq). Consistent with previous data (Fig. 2), the loss of IRE1α led to significant tumor regression in FVB immunocompetent mice (Supplementary Fig. 5A, B).


Furthermore, loss of IRE1α in cancer cells resulted in significantly extended survival of tumor-bearing mice (Supplementary Fig. 5C). After quality filtering, we obtained data from 11071


and 8434 single cells from WT and IRE1α KO tumors, respectively. Upon scRNA-seq clustering analysis, we annotated 8 distinct cell type clusters in addition to the tumor cells (Fig. 4A–C and


Supplementary Fig. 5D). In contrast to WT Myc-CaP tumors, the abundance of pericytes, tumor-associated macrophages (TAMs), and cancer associated fibroblasts (CAFs) were markedly reduced


(50-75%) in IRE1α KO tumors. Sub-clustering of NK Cells + T Cells revealed three different T cell subtypes in addition to NK cells subtypes (Supplementary Fig. 5E). Interestingly, the


abundance of immunosuppressive regulatory T cells (Tregs) (expressing Foxp3, Ctla4 and Pd-1) were markedly reduced in IRE1α KO tumors (Fig. 4C and Supplementary Fig. 5E). There was also a


slight increase in the abundance of CD8+ T cells, while we observed a marked increase in NK cells (Fig. 4C). Among the different cell types in the TME other than cancer cells, TAMs were the


most abundant cell type that constituted around 9% of the tumors. Sub-clustering of TAMs revealed five different clusters expressing distinct gene markers (Fig. 4D and Supplementary Fig. 


6A). Based on the marker gene expression profile reported in a recently published study22, we annotated four clusters as Regulatory TAMs (Reg-TAMs), Inflammatory TAMs (Inflam-TAMs),


Proliferating TAMs (Prolif-TAMs), and IFN primed monocytes/macrophages (IFN-Mo/Mϕ). The fifth cluster, which showed expression of cancer cell markers (likely due to doublets), was


subsequently omitted from further analysis. Reg-TAMs constituted the main TAM subtype accounting for approximately 40% of all TAMs in WT tumor samples (Fig. 4E). Similar to the other TAM


subtypes, their abundance was markedly decreased in IRE1α KO tumors (Fig. 4E). To investigate the origin of the augmented IFN response observed in the TME of IRE1α KO tumors, we conducted


GSEA on each specific cell type annotated within the scRNA-seq data. Genes associated with IFN-α and -γ responses were significantly enriched in all identified TAM subtypes, as well as in


cancer cells and dendritic cells (DCs) (Fig. 4F). There were no significant differences in IFN-α and -γ responses in other cell types (Supplementary Fig. 6B). On the other hand, genes


responsible for protein secretion were significantly enriched among downregulated in IRE1α KO cancer cells, consistent with the proteomics and total RNA-seq data presented earlier (Figs. 2D


and 3F). Taken together, these results indicate that the activation of IRE1α signaling in cancer cells could remodel the TME. A SMALL MOLECULE INHIBITOR OF IRE1Α INHIBITS TUMOR GROWTH IN


SYNGENEIC PCA MOUSE MODELS To assess whether pharmacological inhibition of IRE1α could inhibit PCa growth in a syngeneic setting, we first tested the effect of IRE1α RNase inhibitor MKC8866


on Myc-CaP tumors. Myc-CaP cells were injected into FVB mice, which were either left untreated or treated with MKC8866, and tumor growth was monitored (Fig. 5A). MKC8866 treatment


dramatically reduced Myc-CaP tumor growth (Fig. 5B, C). To model advanced PCa, which commonly involves MYC amplification and PTEN loss in patient tumors23,24,25, we generated a PTEN KO


Myc-CaP cell line by CRISPR-Cas9 genome editing. As expected, the loss of PTEN dramatically accelerated tumor growth in vivo (Fig. 5D, E). Moreover, the expression of immune checkpoint


ligands PD-L1 and B7-H3 was increased in PTEN KO cells (Fig. 5F), consistent with previous studies26,27. Notably, the expression of these ligands was correlated with higher IRE1α mRNA


expression in the TCGA PCa dataset (Fig. 5G). In this model, MKC8866 also effectively suppressed tumor growth, demonstrating its potential translational relevance in advanced PCa as well


(Fig. 5H, I, and K). To further validate our results, we employed RM-1 cells as another syngeneic mouse PCa model, which expresses Ras and Myc oncogenes28. Although, RM-1 tumors grew even


faster than Myc-CaP PTEN KO tumors, MKC8866 was also effective in inhibiting their growth (Fig. 5H, J, and L). Taken together, these findings demonstrate that inhibition of IRE1α signaling


by MKC8866 significantly reduces tumor growth across various syngeneic PCa mouse models, highlighting its translational potential as an effective therapeutic option for PCa. MKC8866


SYNERGIZES WITH ANTI-PD-1 IMMUNE CHECKPOINT BLOCKADE THERAPY Cancer immunotherapy has recently revolutionized therapeutic approaches for various cancer types. For example, immune checkpoint


inhibitors have been highly successful in patients with metastatic melanoma, renal cell carcinoma, head and neck cancer, and non-small cell lung cancer29; however, these therapies have thus


far failed to provide significant clinical benefit for PCa30,31,32,33. Given the significant effect of MKC8866 and activation of the immune microenvironment as exemplified by the augmented


IFN response in IRE1α KO Myc-CaP tumors, we investigated whether MKC8866 could increase responsiveness to anti-PD-1 therapy. We treated Myc-CaP tumor-bearing mice either with MKC8866 (at a


suboptimal dose) alone, an anti-PD-1 antibody alone, or their combination (Fig. 6A). Treatment with either MKC8866 or the anti-PD-1 antibody alone modestly impaired tumor growth, whereas the


combination therapy led to a dramatic inhibition of tumor growth (Fig. 6B, C). We repeated this experiment in the Myc-CaP PTEN KO model and observed similar results (Fig. 6D–F). In the RM-1


model, MKC8866 modestly inhibited tumor growth, whereas the anti-PD-1 antibody treatment had no significant effect; in contrast, the combination therapy exhibited significantly greater


efficacy than MKC8866 treatment alone (Fig. 6G–I). In all PCa mouse models tested, no significant changes in the body weight of mice were observed across the three different mouse models


(Fig. 6J), suggesting that treatments did not cause any apparent toxicity. Taken together, these data suggest that IRE1α inhibition can enhance the therapeutic efficacy of anti-PD-1-based


ICB in PCa. COMBINATION THERAPY MODULATES TAMS AND BOOSTS INFILTRATION OF CD8+ T AND NK CELLS INTO THE TME To date, ICB therapy has largely failed as a potential treatment option in PCa,


except for partial responses in the minority of patients whose tumors have high mutational burden34,35. This has prompted significant efforts to discover the underlying reasons for this


resistance and to develop therapeutic approaches to sensitize PCa to ICB therapy. To explore the mechanisms that underlie the significant therapeutic responses induced by MKC8866 + anti-PD-1


combination therapy, we performed scRNA-seq on tumors from the Myc-CaP PTEN KO experiment described in Fig.  6D–F. We observed notable changes in various cell populations under different


treatment conditions (Fig. 7A, B, and Supplementary Fig. 7A). Treatment with either MKC8866 or anti-PD-1 alone resulted in a marked increase in the abundance of CD8+ T cells and NK cells


(Fig. 7B). Remarkably, combination therapy further boosted the representation of these cells in the TME (Fig. 7B). Analysis of differentially expressed genes in the CD8+ T cell cluster


revealed that combination therapy increased the expression of T cell activation markers such as Gzmb, Prf1, and Nkg7 (Fig. 7C). Expression of Ccl5, a T cell activation marker and


chemoattractant for T and NK cells36, increased more than 32-fold in CD8+ T cells upon combination therapy compared to control tumors (Fig. 7C). These findings underscore the potential of


MKC8866 + anti-PD-1 combination therapy to enhance immune cell infiltration and activation within the TME. In Myc-CaP IRE1α KO tumors there was significantly increased expression of IFN


response genes, especially in TAMs and dendritic cells (Figs. 3 and 4); we therefore investigated whether MKC8866 also augments the IFN response in the TME. GSEA indicated that MKC8866 or


anti-PD-1 treatments induced the expression of genes involved in IFN-α and IFN-γ signaling response in select immune cell types, such as dendritic cells and some TAM subtypes, whereas


combination treatment significantly upregulated both pathways, in almost all immune cell types (Fig. 7D). MKC8866 alone increased the abundance of certain TAM subtypes such as Reg-TAMs and


IFN-Mo/Mϕ in the TME; however, the overall abundance of TAMs was reduced with combination therapy (Supplementary Fig. 7B, C). The exception was for IFN-Mo/Mϕ subset, which exhibited higher


expression levels of MHC-II component genes (H2-aa, H2-Eb1, H2-ab1, and Cd74) (Supplementary Fig. 7D), characteristic of M1-like macrophages37. Interestingly, the combination therapy shifted


the transcriptional profile of all TAM subtypes towards an M1-like phenotype by upregulating the expression of genes involved in MHC-II components, IFN responses (ligp1, Gbp2b, and Cxcl9),


and inflammatory response and activation (AW112010, Nos2, Cd86) (Fig. 7E and Supplementary Fig. 7E). However, the expression of genes associated with M2 macrophages such as Cd63, Spp1, Lyz2,


and Pf4 was downregulated in the TAMs upon combination therapy (Fig. 7E and Supplementary Fig. 7F). In addition to these observations, there was significant increase in the expression of


genes involved in antigen processing and presentation, such as B2m, Cd74, Psmb8, and Psme2 in cancer cells upon combination therapy (Supplementary Fig. 7G). Interestingly, CellChat analysis


of scRNA-seq data from combination therapy showed that the NK cells + T cells cluster has substantial interactions with other cell types, such as cancer cells, macrophages, and dendritic


cells, via the MHC-I signaling pathway (Fig. 7F). Taken together, these data suggest that combination therapy may enhance anti-tumor immunity by reprogramming TAMs to a more pro-inflammatory


and antigen-presenting state. Previous studies have shown that ICB resistance is associated with the upregulation of stress response genes HSPA1A and HSPA1B in CD4+ /CD8+ T cells across


various cancer types35,38. Interestingly, we found that combination therapy reduced the expression of both genes in CD8+ T cells (Fig. 7C and G). Furthermore, in these cells, combination


therapy increased expression of T cell activation markers (Ccl5, Gzmb, Nkg7, and Prf1) and led to a transcriptional profile resembling the profile of CD8+ T cells from anti-PD-1 responsive


patients with mCRPC35 (Figs. 7G, H). These findings highlight the potential of MKC8866 + ICB combination therapy in PCa to modulate CD8+ T cell responses by reducing stress response gene


expression and enhancing activation markers. MYC amplification and PTEN loss have been associated with mCRPC, with PTEN loss reported to generate a more immunosuppressive and ‘cold’ TME,


leading to resistance to ICB therapy39,40,41. Interestingly, comparing the scRNA-seq data from Myc-CaP WT and PTEN KO tumors, we observed that PTEN loss reduced the abundance of CD8+ T and


NK cells in the TME (Supplementary Fig. 8A–C). The abundance of TAMs, especially Reg-TAMs expressing M2-like immunosuppressive markers, such as Arg1, Cd68, Mrc1, and Cd274, markedly


increased in PTEN KO tumors (Supplementary Fig. 8D-F). Moreover, PTEN loss increased the expression of genes involved in mTOR signaling, protein secretion and UPR, and TNFα signaling via


NF-κB response in cancer cells (Supplementary Fig. 8G). In addition, expression of IFN response genes were downregulated in specific TAM subtypes such as IFN-Mo/Mϕ, Prolif-TAMs, Inflam-TAMs


as well as dendritic cells (Supplementary Fig. 8G). In summary, PTEN loss in Myc-CaP tumors diminishes cytotoxic immune cell populations such as CD8+ T cells and NK cells, while concurrently


fostering an immunosuppressive TME, illustrating its complex role in shaping the immune landscape of PCa. IRE1Α-XBP1S PATHWAY IS NEGATIVELY ASSOCIATED WITH CD8+ AND NK CELL INFILTRATION IN


HUMAN PCA To determine if the IRE1α-XBP1s pathway affects immune cell infiltration in the TME, we analyzed the correlation between IRE1α or XBP1s mRNA expression and CD8+ T cell infiltration


in the TCGA PCa dataset. We found that PCa tumors with high IRE1α expression had less CD8+ T cell infiltration (Supplementary Fig. 9A). Additionally, the expression of IRE1α-XBP1s target


genes, such as DNAJB9 and DNAJC3, was negatively correlated with CD8+ T cell infiltration in PCa tumors (Supplementary Fig. 9B, C). Similarly, three different immune cell infiltration


estimation tools suggested that tumors with high XBP1s mRNA levels had significantly lower T cell infiltration (Supplementary Fig. 9D-G). Since the abundance of NK cells were increased in


the scRNA-seq data in the tumors of IRE1α KO and MKC8866 treated animals, we also determined the potential correlation of IRE1α-XBP1s pathway with NK cell infiltration. NK cell infiltration


scores were significantly higher in patients with PCa with low XBP1s expression (Supplementary Fig. 9H-J). Taken together, these findings suggest that IRE1α-XBP1s pathway may play a critical


role in modulating the immune microenvironment in PCa, potentially affecting NK and CD8+ T cells infiltration. TAM GENE SIGNATURE IS ASSOCIATED WITH UNFAVORABLE PCA PROGNOSIS To determine


the potential clinical relevance of the IRE1α-XBP1s pathway on TAMs in the TME, we assessed correlation of IRE1α expression with macrophage markers in the TCGA PCa dataset. IRE1α expression


was positively correlated with the expression of M2 macrophage markers CD163 and MRC1 (Supplementary Fig. 10A). Validating these observations, IHC analyzes showed significantly higher CD68+


tumor infiltrating macrophages in IRE1α high samples (Figs. 8A, B). In line with these, macrophage levels were estimated to be higher in PCa tumors with high XBP1s mRNA levels (Fig. 8C)42.


Through the examination of 147 genes that exhibited significant downregulation in TAMs between WT and IRE1α KO tumors (Supplementary Data 3), we identified five genes (Apoe, C1qa, Trem2,


Pf4, and Mrc1) (Fig. 8D) with high expression mainly in the macrophage cluster (Supplementary Fig. 10B). These genes were combined with TAM gene markers Arg1 and Cd68, which were among the


top gene markers in Reg-TAMs (Supplementary Fig. 6A), the most abundant TAM subtype within the TME that also increased in the PTEN KO Myc-CaP TME compared to wild type tumors (Supplementary


Fig. 8E, F). We established a scRNA-seq derived “TAM signature” from these 7 genes. Notably, the expression of TAM signature genes was increased in the macrophage cluster in PTEN KO tumors


(Supplementary Fig. 10C). Interestingly, in these tumors, combination therapy reduced the expression of TAM signature genes (Supplementary Fig. 10D), which were mainly expressed in Reg-TAMs


(Supplementary Fig. 10E). Importantly, the TAM gene signature was elevated in patient samples with advanced-stage PCa, showed strong positive association with the Gleason score and


negatively correlated with progression-free survival in the TCGA and disease-free survival in MSKCC PCa datasets15,43 (Fig. 8E–G). Collectively, these findings demonstrate a significant


association of the TAM gene signature with an unfavorable outcome in PCa. DISCUSSION During tumor formation, cancer cells hijack the normal stress response pathways to adapt to various


stressors that impact the TME. Similar to other solid tumors, PCa cells rely on UPR signaling to survive under intra- and extra-cellular stress conditions16,19. While the role of UPR in


promoting cancer cell growth and malignancy is documented in various studies3,16,44,45,46,47,48,49, the complex relationship between the activation of UPR in cancer cells, its influence on


the TME and the anti-tumor immune responses remain largely unexplored. In this study, we have shown that inhibition of the IRE1α signaling in PCa cells remodels the TME by increasing IFN


responses and decreasing the abundance of immunosuppressive cells. Pharmacological inhibition of IRE1α signaling also increased IFN responses and significantly improved anti-tumor immunity.


These findings are significant since PCa is considered an immunologically “cold” tumor, with very limited response to immunotherapies compared with some other cancer types. IRE1α loss


resulted in significant tumor regression and enhanced survival in mice harboring Myc-CaP tumors. scRNA-seq analysis showed that the abundance of immunosuppressive cells in the TME, such as


TAMs and Tregs, were markedly reduced in IRE1α KO tumors compared to their WT counterparts, while CD8+ T and NK cells were increased (Fig. 4; for a model, see Fig. 9). This indicates a


notable remodeling of the TME upon IRE1α loss in tumor cells. A recent study in a non-small cell lung cancer (NSCLC) mouse model found that upon IRE1α targeting, CD8+ T cells were increased


and Tregs were decreased, but without significant effects on TAMs44, suggesting that there may be both similarities and differences for the influence of tumor cell IRE1α signaling on the TME


in different cancer types. IFNs are cytokines that help the immune system eradicate pathogens and cancer cells. They activate immune cells such as NK cells, cytotoxic T cells, and


macrophages, and enhance anti-tumor immunity by increasing MHC antigen presentation20,50,51,52,53,54. Genetic or pharmacological inhibition of IRE1α led to marked increases in IFN signaling


in multiple cell types in the TME, especially in antigen-presenting cells such as TAMs and DCs. It is possible that targeting IRE1α may promote immunogenic cell death and production of IFNs


in DCs to convert them into inflammatory cells, which then can stimulate T and NK cell infiltration resulting in anti-tumor immunity, similar to what has been observed for the PERK arm of


UPR in melanoma cells53. Further work is required to assess this possibility. Similar to other immunologically ‘cold’ and immunotherapy unresponsive tumor types, PCa has a strong


immunosuppressive TME, which contains scarce but high PD-1-expressing tumor infiltrating lymphocytes55,56,57,58. In addition, they can have high levels of Tregs, TAMs and myeloid derived


suppressor cells (MDSCs) all of which are linked to disease progression and death55,56,58,59. Notably, PTEN deletion in Myc-CaP cells led to a more immunosuppressive and ‘cold’ TME,


characterized by diminished IFN responses, lower infiltration of CD8+ T cells and NK cells, and a higher abundance of TAMs, especially M2-like macrophages with immunosuppressive marker gene


expression. Remarkably, these effects were reversed by MKC8866 combined with anti-PD-1 therapy in Myc-CaP PTEN KO syngeneic mouse PCa model. Thus, our data highlight the impact of targeting


IRE1α to reprogram the TME and enhance the efficacy of anti-PD-1 immunotherapy in this advanced PCa model. Previous reports have linked HSPA1A and HSPA1B expression in CD4+ /CD8+ T cells to


ICB therapy resistance in various cancers35,38. Interestingly, MKC8866 + anti-PD-1 therapy reduced HSPA1A and HSPA1B expression in CD8+ T cells and increased the expression of T cell


activation markers. It is important to highlight that with MKC8866 + anti-PD-1 therapy, CD8+ T cells also begin to increase the expression of various inhibitory immune checkpoint receptors


such as Lag3, Pdcd1, Havcr2 (Figs. 7G, H), suggesting chronic antigen stimulation and CD8+ T cell exhaustion in the TME60. Given ongoing clinical trials exploring dual combinations targeting


LAG3, TIM-3, and PD-1/PD-L1 immune checkpoints and bispecific antibodies that simultaneously target LAG3/PD-1 or TIM-3/PD-1, combinatorial inhibition of IRE1α and LAG3 or TIM-3, with or


without PD-1/PD-L1 inhibition could significantly reprogram the TME and augment the efficacy of bispecific antibodies61,62. Thus, it would be valuable to investigate whether coupling MKC8866


to anti-PD-1 or bispecific ICB antibody therapies can enhance the efficacy of ICB therapy and reverse resistance in the clinical setting, not only for PCa but also for other cancer types.


Over the past decade, the success of immunotherapies has transformed the landscape of cancer treatment for certain cancer types. However, despite sipuleucel-T being the first ever-approved


cancer vaccine and the first immunotherapy licensed for PCa in 2010, it demonstrated limited success in the clinic and no other immunotherapies have been approved for PCa to date. The data


we present here highlight the potential of targeting IRE1α as a therapeutic strategy to enhance anti-PD-1 immunotherapy in PCa. Since MKC8866 is now in clinical trials (NCT03950570) in


patients with advanced cancer, our results suggest that new clinical trials can be designed for PCa where immunotherapy approaches are coupled to IRE1α targeting. Recent work has shown that


targeting IRE1α signaling increased ICB efficacy in ovarian, breast, and colon cancer models, but not with a small molecule inhibitor in clinical trials63,64,65; in addition, the molecular


mechanisms appear to be different then we present here. Further work will be required to evaluate if MKC8866 would also synergize with ICB in other cancer types. In addition to therapeutic


implications, the TAM gene signature that we have identified has significant prognostic power in patients with PCa and is reduced by the MKC8866 + anti-PD-1 combination therapy; this


suggests that it can potentially be useful in predicting disease course and thereby adjusting therapy options. For example, the TAM gene signature may help in stratifying patients with PCa


for anti-PD-1 immunotherapy. Further work is necessary to assess these possibilities. Previous studies have shown that IRE1α, beyond its role in splicing XBP1, has other catalytic and


non-catalytic functions. These include RIDD activity, which degrades specific target mRNAs and microRNAs, and serving as a structural determinant of mitochondria-associated membranes66,67.


In cancer, RIDD activity can influence tumor progression and response to therapy47,68. We attempted to assess RIDD activity upon genetic or pharmacological inhibition of IRE1α in our RNA-seq


datasets. When we analyzed our data using two different RIDD activity gene signatures, developed for multiple myeloma and glioblastoma47,69, the results were inconsistent, suggesting that


these signatures maybe context dependent and their applicability to PCa might be limited. On the other hand, in IRE1α KO tumors or upon MKC8866 treatment, there was significant


downregulation of genes that regulate oxidative phosphorylation, which could be due to disrupted mitochondrial Ca2+ levels (Fig. 3F, Supplementary Figs. 4D, 7D). Further functional studies


are required to investigate whether RIDD or the non-catalytic functions of IRE1α have implications in PCa. In summary, our findings demonstrate that activation of IRE1α signaling in cancer


cells not only has a pivotal role in cancer growth and survival, but it also reprograms the TME by modulating anti-tumor immune responses. This opens up the possibility that targeting IRE1α


may be a potential approach to improve immune therapy options for PCa, as well as for other cancer types. METHODS Our research adheres to all applicable ethical guidelines. All animal


studies were performed according to the experimental protocol (mouse strain, animal sex, age, number of animals allowed, and housing) that was approved by University of Oslo Institutional


Animal Care and Use Ethical Committee (FOTS ID 27414) and the Danish Animal Experiments Inspectorate (license no. 2020-15-0201-00711). The maximal tumor size/burden was not exceeded maximum


tumor size of 1 or 2 cubic centimeters for orthotopic and subcutaneous tumor models, respectively. All patient specimens were obtained with written informed consent and the approval from


University of British Columbia Clinical Research Ethics Board (REB number: H21-03722) and the Norwegian Regional Committees for Medical Research Ethics South-East region (REK number


S-07443a). CELL CULTURE Myc-CaP (#CRL-3255) and RM-1 (#CRL-3310) cells were purchased from ATCC and cultured in DMEM supplemented with 10% fetal bovine serum (FBS), penicillin and


streptomycin. Cells were maintained in a humidified incubator at 37 °C with 5% CO2. Lack of _Mycoplasma_ contamination was regularly confirmed. CRISPR-CAS9 GENOME EDITING To generate the


IRE1α and PTEN knock-out (KO) Myc-CaP cell lines, a guide RNA (gRNA) (5’-GCTTGCATGCTGTTAGCAAG-3’) targeting IRE1α or (5’-GCTAACGATCTCTTTGATGA-3’) PTEN were cloned into the PX458 plasmid


which expresses green fluorescent protein (GFP) as a marker70,71,72. Cells were transfected in 6-well plates using Lipofectamine 3000 (ThermoFisher Scientific). After 72 h, GFP-positive


cells were FACS sorted into 96-well plates as single-cell per well. Growing cells were transferred into 24-well plates and further grown to verify their identity by assessing IRE1α and XBP1s


expression. LENTIVIRUS PRODUCTION AND ECTOPIC XBP1S EXPRESSION HEK293T cells were transfected with pMD2.G (Addgene plasmid #12259), psPAX2 (Addgene plasmid #12260), and either


pLV[Exp]-Puro-EF1A > EGFP (Vectorbuilder # VB010000-9483amc) or pLV[Exp]-EGFP-EF1A>mXbp1s (Vectorbuilder # VB900142-6935cag) using Lipofectamine 3000 (Thermo Fisher Scientific)


following the manufacturer’s instructions. After 12 h, the growth media was replaced. Lentiviral particles were harvested at 48 and 72 h post-transfection, filtered through a 0.2 µM filter,


and stored at –80 °C. Myc-CaP IRE1α WT and KO (clone #1) cells were infected with the lentiviral particles, and GFP-positive cells were FACS sorted into 96-well plates as single-cell per


well. Individual colonies with similar levels of GFP expression were selected, and overexpression of XBP1s and its target genes expression were validated using qRT-PCR analysis. CELL


VIABILITY AND COLONY FORMATION ASSAY Myc-CaP IRE1α wild type (WT) and knock-out (KO) clones were seeded into 96-well plates. After 72 h, cell viability was determined by the CCK-8 assay. For


thapsigargin (TG) sensitivity, where indicated, cells were treated with 100 nM TG for the indicated times and cell viability was determined using the CCK-8 assay. For the colony formation


assay, cells were trypsinized and seeded into 6-well plates. After 4 days, cells were fixed with methanol and stained with 0.4% crystal violet and visualized. APOPTOSIS AND CELL CYCLE ASSAY


Apoptosis assay was performed as described previously with some minor changes73. Briefly, Myc-CaP IRE1α WT and KO clones were seeded into 6 well plates. Cells were left untreated or treated


with TG (100 nM) for 12 h. Alternatively, Myc-CaP cells were either treated with TG (100 nM) or MKC8866 (10 µM) alone, or their combination for 12 h. After 12 h, cells were trypsinized and


washed with cell staining buffer (Biolegend # 420201). 100 µl of cells in Annexin V binding buffer (Biolegend # 422201) were stained with 3 ul of 7-AAD viability staining solution (Biolegend


#420404) and Annexin V (Biolegend #640953) for 15 min at room temperature. Cells were analyzed using BD LSR II Flow Cytometer. Data were analyzed using Kaluza Analysis Software (Beckman


Colter Life Sciences). Cell cycle assay was performed as described previously with some minor changes74. Briefly, cells were resuspended by adding –20 °C pre-cooled methanol drop by drop


while gently vortexing. Cells were fixed overnight at –20 °C in methanol. Next day, fixed cells were washed with PBS and resuspended in staining buffer (PBS containing 1.5 µg/ml Hoechst


33258 and 100 µg/ml RNase A). Cells were analyzed using BD LSR II Flow Cytometer. Data were analyzed using Kaluza Analysis Software (Beckman Colter Life Sciences). MOUSE STUDIES Briefly, 1 ×


 106 Myc-CaP cells were mixed 1:1 with matrigel (BD Biosciences) and the mixture was inoculated subcutaneously into 4-week-old male FVB/NRj (Janvier Labs) or nude mice (BALB/c Nu/Nu,


in-house breeding) in both hind flanks. For the nude mice experiments, three animals per group was used as less variability observed in tumor growth. For MKC8866 treatment, 1 × 106 Myc-CaP


cells or 0.5 × 106 Myc-CaP PTEN KO cells were injected into FVB mice as described above. For RM-1 cells, 0.5 × 106 cells were injected into 4-week-old male C57BL/6j (Janvier Labs) as


described above. When the tumors were palpable, mice were randomized and either treated with vehicle (0.5% w/v hydroxypropyl-methylcellulose dissolved in water plus 0.2% v/v Tween-80,


adjusted to pH 4.0) or MKC8866 (300 mg/kg) by oral gavage every other day (Myc-CaP model) or daily (Myc-CaP PTEN KO and RM-1 models) until the end of the experiment. For anti-PD-1 and


MKC8866 combination treatment, 1 × 106 Myc-CaP cells were injected into FVB mice as described above. When the tumors were palpable (after 7 days of post-injection), mice were randomized and


either treated with vehicle or MKC8866 (150 mg/kg) by oral gavage every other day. After 3 days of initial MKC8866 treatment, mice were treated weekly with either 10 mg/kg of anti-PD-1


(ichorbio # ICH1132UL) or anti-IgG isotype control (ichorbio # ICH2244UL) antibodies by intraperitoneal injection (IP). In PTEN KO and RM-1 models, tumor-bearing mice were treated daily with


MKC8866 (300 mg/kg) along with anti-IgG or anti-PD-1 antibodies administered every three days or every two days, respectively as described above. Tumors were measured with a caliper at the


indicated time points and volumes were calculated using the following formula V = W2 × L × 0.5 (V, volume; W, width; L, length). Tumors were harvested at the end of the experiment and


weights were determined. For survival experiments, the tumor bearing mice were euthanized when the tumor sizes reached to maximum ethically allowable limit. For the orthotopic Myc-CaP model,


0.5 × 106 cells were injected into the anterior prostatic lobe of FVB mice in 2/3 PBS and 1/3 Matrigel gel (Sigma #E6909). Scans were performed using a T1 weighted MRI scan before


termination to evaluate the tumor volume as described previously75. RNA-SEQUENCING RNA-sequencing (RNA-seq) was performed on Myc-CaP IRE1α WT and KO cells, as well as corresponding tumor


samples from FVB mice. Briefly, RNA was isolated from the cells grown in vitro or the tumor samples using Trizol and purified using RNeasy columns (Qiagen). After RNA isolation, TruSeq


stranded RNA-seq libraries were prepared according to manufacturer instructions (Illumina) and 50 bp paired-end sequencing was performed using Illumina NovaSeq (Illumina, Inc) at the NorSeq


Sequencing Core (Ullevål). RNA-SEQ AND GENE SET ENRICHMENT ANALYSIS RNA-Seq data was processed through nf-core rnaseq pipeline (version 3.6)76. Briefly, reads were trimmed using Trimgalore


(version 0.6.7) and then mapped to Grcm38 using STAR RNA-Seq aligner (version 2.6.1d). The reads were then quantified via Salmon (version 1.5.2) using Ensemble 81 as a reference transcript


database. Reads were normalized via DeSeq normalization77. Gene set enrichment analysis (GSEA) tool and Enrichr web server were used to infer pathways and gene networks of differentially


expressed genes78,79,80,81. QUANTITATIVE AND SEMI-QUANTITATIVE PCR For reverse transcription quantitative PCR (qPCR) analysis, total RNA was isolated as described above. One μg of RNA was


reverse-transcribed using Superscript II (Thermo Fisher Scientific) and diluted 1/20 in nuclease-free water. qPCR was performed as described previously5 from diluted cDNAs with gene specific


primers (Supplementary Data 4). Relative gene expression was determined by ∆∆CT method by normalizing mRNA expression values to β-actin. Semi-quantitative reverse transcription PCR (RT-PCR)


was performed to determine XBP1s mRNA levels. XBP1 specific primers spanning the XBP1s splicing site were used to detect both XBP1 and XBP1s transcript levels with distinct PCR products.


WESTERN ANALYSIS Western analysis was performed as described previously5,82. Briefly, cells were lysed in RIPA buffer supplemented with protease and phosphatase inhibitor cocktail. 30 μg of


cleared protein lysate was resolved by 10% SDS-PAGE. Proteins were transferred onto a PVDF membrane (Biorad) and blocked for 1 h with 5% skim milk, followed by overnight incubation with


primary antibodies from Cell Signaling, IRE1α (#3294S; 1/1000), PTEN (#9559S; 1/1000) and β-Actin (#3700, 1/20000). Membranes were washed and incubated with secondary horseradish


peroxidase-conjugated anti-rabbit IgG or anti-mouse IgG antibodies at room temperature for 1 h. ECL detection reagents (Amersham Pharmacia Biotech) were used to visualize the proteins. IHC


TISSUE MICROARRAY OF PCA PATIENT SAMPLES A tissue microarray (TMA), containing benign prostate (_n_ = 16) and prostate cancer (_n_ = 72) samples, was obtained from Vancouver Prostate Center.


TMA sections were analyzed for IRE1α, CD68 and AR immunoexpression using Ventana Discovery Ultra autostainer (Ventana Medical Systems, Tucson, Arizona). In brief, baked and deparaffinized


tissue sections were incubated in Tris-based buffer (CC1, Ventana) at 95 °C for 64 min to retrieve antigenicity, followed by incubation at room temperature with anti-IRE1α Rabbit mAb (Clone


14C10, #3294; Cell Signaling, 1/50), CD68 Mouse mAb (Clone Kp-1, #168M-95; Cell Marque, 1/200), and AR (Clone N-20, sc-816, Santa Cruz, 1/50) for 2 h. Bound primary antibodies were


visualized with the DISCOVERY Anti-Rb HQ/Anti-HQ HRP Detection Kit, DAB MAP Detection kit, and UltraMap DAB anti-Rb Detection Kit (Ventana) respectively. All stained slides were digitized


with Leica scanner (Aperio AT2, Leica Microsystems; Concord, Ontario, Canada) at magnification equivalent to 40X. The images were subsequently stored in the Aperio eSlide Manager (Leica


Microsystems) of the Vancouver Prostate Center. The IRE1α and AR IHC positive areas and CD68 positive cell counts were reviewed by a research pathologist. Values on a four-point scale were


assigned to IRE1α and nuclear AR immunostaining. Descriptively, 0 represents no staining, 1 represents low, but detectable degree of staining, 2 represents clearly positive cases, and 3


represents strong expression. For IRE1α, IHC was further quantified for staining intensity (0–3) and percentage of positive cells (0–100%). For each sample, the H-Score was calculated as


staining intensity x percentage of positive cells. PROTEOMICS Whole tumor tissue protein extracts were prepared and subjected to an in-solution tryptic digest using a modified version of the


Single-Pot Solid-Phase-enhanced Sample Preparation (SP3) protocol83,84. Eluates were added to Sera-Mag Beads (Thermo Scientific) in 10 µl 15% formic acid and 30 µl of ethanol and proteins


were bound by shaking for 15 min at room temperature. SDS was removed by four subsequent washes with 200 µl of 70% ethanol. Proteins were digested overnight at room temperature with 0.4 µg


of sequencing grade modified trypsin (Promega, #V5111) in 40 µl HEPES/NaOH, pH 8.4 in the presence of 1.25 mM TCEP and 5 mM chloroacetamide (Sigma-Aldrich). Beads were separated, washed with


10 µl of an aqueous solution of 2% DMSO and the combined eluates were dried down. Peptides were reconstituted in 10 µl of ddH2O and reacted for 1 h at room temperature with 40 µg of TMTpro


label reagent (Thermo Scientific) dissolved in 4 µl of acetonitrile. Excess TMT reagent was quenched by the addition of 4 µl of an aqueous 5% hydroxylamine solution (Sigma). Peptides were


reconstituted in 0.1% formic acid, mixed to achieve a 1:1 ratio across all TMT-channels and purified by a reverse phase clean-up step (OASIS HLB 96-well µElution Plate, Waters #186001828BA).


Peptides were subjected to an off-line fractionation under high pH conditions yielding 12 fractions83. Each fraction was analyzed by LC-MS/MS on an Orbitrap Fusion Lumos mass spectrometer


(Thermo Scientific). To this end, peptides were separated using an Ultimate 3000 nano RSLC system (Dionex) equipped with a trapping cartridge (Precolumn C18 PepMap100, 5 mm, 300 μm i.d., 5 


μm, 100 Å) and an analytical column (Acclaim PepMap 100. 75 × 50 cm C18, 3 mm, 100 Å) connected to a nanospray-Flex ion source. The peptides were loaded onto the trap column at 30 µl per min


using solvent A (0.1% formic acid) and eluted using a gradient from 2 to 40% Solvent B (0.1% formic acid in acetonitrile) over 2 h at 0.3 µl per min (all solvents were of LC-MS grade). The


Orbitrap Fusion Lumos was operated in positive ion mode with a spray voltage of 2.4 kV and capillary temperature of 275 °C. Full scan MS spectra with a mass range of 375–1500 m/z were


acquired in profile mode using a resolution of 120,000 (maximum fill time of 50 ms or a maximum of 4e5 ions (AGC) and a RF lens setting of 30%. Fragmentation was triggered for 3 s cycle time


for peptide like features with charge states of 2–7 on the MS scan (data-dependent acquisition). Precursors were isolated using the quadrupole with a window of 0.7 m/z and fragmented with a


normalized collision energy of 38. Fragment mass spectra were acquired in profile mode and a resolution of 30,000. Maximum fill time was set to 64 ms or an AGC target of 1e5 ions. The


dynamic exclusion was set to 45 s. PROTEOMICS DATA ANALYSIS Acquired data were analyzed using IsobarQuant85 and Mascot V2.4 (Matrix Science) using a reverse UniProt FASTA Mus musculus


database (UP000000589, downloaded in May 2016, 59.754 entries including common contaminants). The following modifications were taken into account: Carbamidomethyl (C, fixed), TMT16plex (K,


fixed), Acetyl (N-term, variable), Oxidation (M, variable) and TMT16plex (N-term, variable). The mass error tolerance for full scan MS spectra was set to 10 ppm and for MS/MS spectra to 0.02


 Da. A maximum of two missed cleavages were allowed. A minimum of two unique peptides with a peptide length of at least seven amino acids and a FDR below 0.01 were required on the peptide


and protein level86. The raw output files of IsobarQuant (protein.txt–files) were processed using the R programming language (ISBN 3-900051-07-0). Only proteins that were quantified with at


least two unique peptides were considered for the analysis. 6505 proteins passed the quality control filters. Raw TMT reporter ion intensities (‘signal_sum’ columns) were first cleaned for


batch effects using limma87 and further normalized using vsn (variance stabilization normalization)88. Proteins were tested for differential expression using the limma package. The replicate


information was added as a factor in the design matrix given as an argument to the ‘lmFit’ function of limma. A protein was annotated as a hit with a false discovery rate (fdr) < 5% and


a fold-change of at least 100% and as a candidate with a fdr below 20% and a fold-change of at least 50%. SINGLE-CELL SUSPENSION OF TUMOR SAMPLES FOR SCRNA-SEQ Freshly dissected tumor


samples were washed with PBS and transferred into GEXSCOPE Tissue Preservation Solution (Singleron), and stored on ice. 6 tumor samples from each group were pooled for scRNA-seq. The tissue


samples were then washed with Hanks balanced salt solution three times and minced into small pieces which were digested with Tissue Dissociation Solution (Singleron) at 37 °C for 15 min with


agitation. After digestion, cells were passed through a 40 μm strainer to remove cell debris and centrifuged at 300 × _g_ for 5 min. The supernatant was removed and cell pellet was


resuspended in 1 ml PBS, followed by red blood cell lysis using RBC lysis buffer. Next, cells were centrifuged at 500 × _g_ for 5 min and resuspended in PBS. The viability and cell count


were determined by trypan blue staining under microscope. Cell viability exceeded 80% for each sample. SCRNA-SEQ LIBRARY PREPARATION The single-cell suspension was adjusted to a


concentration of 1 × 105 / ml for library preparation. This was loaded onto microfluidics chips (GEXSCOPE Single-Cell RNA-seq Kit, Singleron Biotechnologies) and GEXSCOPE 3´SD scRNA-seq


libraries were prepared according to manufacturer instructions (Singleron Biotechnologies). Individual libraries were diluted and pooled for next generation sequencing on Illumina NovaSeq


with 150 bp paired-end reads. SCRNA-SEQ DATA ANALYSIS The sequencing reads were aligned to mouse reference Grcm38 genome and Singleron Celescope tool


(https://github.com/singleron-RD/CeleScope) were used to generate the gene expression matrices. Gene expression levels were analyzed by counting the unique molecular indices (UMIs) detected


in each cell. We applied quality control measures to the data. Only cells with a library size of at least 1,000 counts and falling within the 95 percent confidence interval for the


prediction of mitochondrial content ratio and detected genes in proportion to the cell’s library size were retained. In addition, cells with mitochondrial proportions greater than 10% were


removed. To normalize, scale, and reduce the dimensionality of the scRNA-seq data, Seurat was used with default functions and parameters, including Principal Component Analysis (PCA)89.


Harmony was used for data integration, and the top 50 dimensions returned by Harmony were used to generate UMAP projections of the data90. Cell clustering was performed using Seurat’s


built-in functions, with default resolution and utilizing the Harmony embedding as the basis for constructing the nearest neighbor network. Cell identities were assigned through differential


expression analysis and manual inspection of cell type-specific marker genes. For visualizing quantitative gene expression and specific gene expression patterns, R packages ggplot2 and


Nebulosa were employed91. GSEA analysis was performed as described previously92. Cell-cell communication across different experimental conditions was analyzed using the CellChat R package.


Initially, a Seurat object containing all treatment data was integrated to serve as input for generating a CellChat object, utilizing the count data and associated metadata. For reference,


the CellChatDB.mouse database was employed to identify known signaling pathways and interactions relevant to mouse cell communication. The dataset was subsetted to align with the reference,


enabling the detection of overexpressed genes and interactions potentially involved in cell communication. Communication probabilities were calculated at both the interaction and pathway


levels, and interactions involving fewer than ten cells were excluded to ensure robust results. An aggregated network of interactions was then constructed and analyzed to compute centrality


measures, which identified the key roles of specific cell types or interactions within the network. This methodology was applied to multiple subsets of the Seurat object, each representing


distinct experimental conditions. For each experimental group, bubble plots and heatmaps were generated to highlight significant interactions. A predefined list of signaling pathway for


“MHC-I” was examined for each experimental conditions. Chord diagrams were created to visualize interactions, providing a clear depiction of cell-cell communication dynamics under each


experimental scenario. BIOINFORMATICS ANALYSIS Raw data for PCa RNA-seq were downloaded from the TCGA website. Raw reads were processed through the nf-core rnaseq pipeline (version 3.6)76.


Briefly, reads were trimmed using Trimgalore (version 0.6.7), and then mapped to Grch38 using the STAR RNA-Seq aligner (version 2.6.1d). Finally, XBP1s reads were obtained from the STAR’s


splice junction output file (SJ.out.tab files) using the following coordinates: Chr22:28796128-28796153. XBP1s specific reads were normalized to the total number of mapped read counts. Next,


using normalized spliced XBP1s reads, samples were divided into low (_n_ = 200) and high (_n_ = 200) expressing groups. GSEA was performed as previously described78. Immune cell


infiltration analysis was performed by downloading the TCGA PCa infiltration estimation data from the TIMER2.0 website93. Immune cell infiltration estimation scores of XBP1s low and high


expressing PCa were extracted and analyzed. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA


AVAILABILITY RNA-seq and scRNA-seq data generated in this study have been deposited in the GEO database with the accession code GSE240383. Publicly available RNA-seq datasets were used with


accession code GSE141633 and TCGA primary PCa raw data was downloaded from Genomic Data Commons Data Portal [https://portal.gdc.cancer.gov/]. The mass spectrometry proteomics data generated


in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD044287. Immune cell infiltration estimation data for PCa


patient samples was downloaded from TIMER2.0 website [http://timer.cistrome.org/]. The remaining data are available within the Article, Supplementary Information or Source Data file. Source


data are provided with this paper. REFERENCES * Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. _Science_ 334, 1081–1086 (2011).


Article  ADS  CAS  PubMed  Google Scholar  * Hetz, C., Zhang, K. & Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. _Nat. Rev. Mol. Cell Biol._ 21,


421–438 (2020). Article  CAS  PubMed  PubMed Central  Google Scholar  * Marciniak, S. J., Chambers, J. E. & Ron, D. Pharmacological targeting of endoplasmic reticulum stress in disease.


_Nat. Rev. Drug Discov._ 21, 115–140 (2022). Article  CAS  PubMed  Google Scholar  * Sheng, X. et al. Divergent androgen regulation of unfolded protein response pathways drives prostate


cancer. _EMBO Mol. Med_ 7, 788–801 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Sheng, X. et al. IRE1alpha-XBP1s pathway promotes prostate cancer by activating c-MYC


signaling. _Nat. Commun._ 10, 323 (2019). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Di Conza, G., Ho, P. C., Cubillos-Ruiz, J. R. & Huang, S. C. Control of immune cell


function by the unfolded protein response. _Nat. Rev. Immunol_. 23, 546–562 (2023). * Martinon, F., Chen, X., Lee, A. H. & Glimcher, L. H. TLR activation of the transcription factor


XBP1 regulates innate immune responses in macrophages. _Nat. Immunol._ 11, 411–418 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Mogilenko, D. A. et al. Metabolic and


innate immune cues merge into a specific inflammatory response via the UPR. _Cell_ 178, 263 (2019). Article  CAS  PubMed  Google Scholar  * Chopra, S. et al. IRE1alpha-XBP1 signaling in


leukocytes controls prostaglandin biosynthesis and pain. _Science_ 365. https://doi.org/10.1126/science.aau6499 (2019). * Chen, X. & Cubillos-Ruiz, J. R. Endoplasmic reticulum stress


signals in the tumour and its microenvironment. _Nat. Rev. Cancer_ 21, 71–88 (2021). Article  CAS  PubMed  Google Scholar  * Dong, H. et al. The IRE1 endoplasmic reticulum stress sensor


activates natural killer cell immunity in part by regulating c-Myc. _Nat. Immunol._ 20, 865–878 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cubillos-Ruiz, J. R. et al. ER


stress sensor XBP1 controls anti-tumor immunity by disrupting dendritic cell homeostasis. _Cell_ 161, 1527–1538 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Song, M. et


al. IRE1alpha-XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity. _Nature_ 562, 423–428 (2018). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  *


Phillips, J. W. et al. Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers. _Proc. Natl Acad. Sci. USA_ 117, 5269–5279 (2020).


Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Cancer Genome Atlas Research, N. The molecular taxonomy of primary prostate cancer. _Cell_ 163, 1011–1025 (2015). Article  Google


Scholar  * Jin, Y. & Saatcioglu, F. Targeting the unfolded protein response in hormone-regulated cancers. _Trends Cancer_ 6, 160–171 (2020). Article  CAS  PubMed  Google Scholar  *


Watson, P. A. et al. Context-dependent hormone-refractory progression revealed through characterization of a novel murine prostate cancer cell line. _Cancer Res_ 65, 11565–11571 (2005).


Article  CAS  PubMed  Google Scholar  * Ellwood-Yen, K. et al. Myc-driven murine prostate cancer shares molecular features with human prostate tumors. _Cancer Cell_ 4, 223–238 (2003).


Article  CAS  PubMed  Google Scholar  * de la Calle, C. M., Shee, K., Yang, H., Lonergan, P. E. & Nguyen, H. G. The endoplasmic reticulum stress response in prostate cancer. _Nat. Rev.


Urol._ 19, 708–726 (2022). Article  PubMed  Google Scholar  * Gocher, A. M., Workman, C. J. & Vignali, D. A. A. Interferon-gamma: teammate or opponent in the tumour microenvironment?


_Nat. Rev. Immunol._ 22, 158–172 (2022). Article  CAS  PubMed  Google Scholar  * Ayers, M. et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. _J. Clin.


Invest_ 127, 2930–2940 (2017). Article  PubMed  PubMed Central  Google Scholar  * Ma, R. Y., Black, A. & Qian, B. Z. Macrophage diversity in cancer revisited in the era of single-cell


omics. _Trends Immunol._ 43, 546–563 (2022). Article  CAS  PubMed  Google Scholar  * Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. _Cell_ 161, 1215–1228


(2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Grasso, C. S. et al. The mutational landscape of lethal castration-resistant prostate cancer. _Nature_ 487, 239–243 (2012).


Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Abida, W. et al. Genomic correlates of clinical outcome in advanced prostate cancer. _Proc. Natl Acad. Sci. USA_ 116, 11428–11436


(2019). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Anker, J. F. et al. Multi-faceted immunomodulatory and tissue-tropic clinical bacterial isolate potentiates prostate


cancer immunotherapy. _Nat. Commun._ 9, 1591 (2018). Article  ADS  PubMed  PubMed Central  Google Scholar  * Shi, W. et al. Immune checkpoint B7-H3 is a therapeutic vulnerability in prostate


cancer harboring PTEN and TP53 deficiencies. _Sci. Transl. Med_ 15, eadf6724 (2023). Article  CAS  PubMed  PubMed Central  Google Scholar  * Baley, P. A., Yoshida, K., Qian, W., Sehgal, I.


& Thompson, T. C. Progression to androgen insensitivity in a novel in vitro mouse model for prostate cancer. _J. Steroid Biochem Mol. Biol._ 52, 403–413 (1995). Article  CAS  PubMed 


Google Scholar  * Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. _Science_ 359, 1350–1355 (2018). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  *


de Almeida, D. V. P., Fong, L., Rettig, M. B. & Autio, K. A. Immune checkpoint blockade for prostate cancer: niche role or next breakthrough? _Am. Soc. Clin. Oncol. Educ. Book_ 40, 1–18


(2020). PubMed  Google Scholar  * Sharma, P. et al. Nivolumab plus ipilimumab for metastatic castration-resistant prostate cancer: preliminary analysis of patients in the checkmate 650


trial. _Cancer Cell_ 38, 489–499.e483 (2020). Article  CAS  PubMed  Google Scholar  * PD-1 Blockade falls short (Repeatedly) in prostate cancer. _Cancer Discov._ 13, 1032–1033.


https://doi.org/10.1158/2159-8290.CD-NB2023-0017 (2023). * Powles, T. et al. Atezolizumab with enzalutamide versus enzalutamide alone in metastatic castration-resistant prostate cancer: a


randomized phase 3 trial. _Nat. Med_ 28, 144–153 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Graf, R. P. et al. Comparative effectiveness of immune checkpoint inhibitors


vs chemotherapy by tumor mutational burden in metastatic castration-resistant prostate cancer. _JAMA Netw. Open_ 5, e225394 (2022). Article  PubMed  PubMed Central  Google Scholar  * Guan,


X. et al. Androgen receptor activity in T cells limits checkpoint blockade efficacy. _Nature_ 606, 791–796 (2022). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Song, A.,


Nikolcheva, T. & Krensky, A. M. Transcriptional regulation of RANTES expression in T lymphocytes. _Immunol. Rev._ 177, 236–245 (2000). Article  CAS  PubMed  Google Scholar  * Sica, A.


& Mantovani, A. Macrophage plasticity and polarization: in vivo veritas. _J. Clin. Invest_ 122, 787–795 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Chu, Y. et al.


Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. _Nat. Med_ 29, 1550–1562 (2023). Article  CAS  PubMed  PubMed Central  Google Scholar  * Peng, W.


et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. _Cancer Discov._ 6, 202–216 (2016). Article  CAS  PubMed  Google Scholar  * Bezzi, M. et al. Diverse genetic-driven


immune landscapes dictate tumor progression through distinct mechanisms. _Nat. Med_ 24, 165–175 (2018). Article  CAS  PubMed  Google Scholar  * Qi, Z. et al. Overcoming resistance to immune


checkpoint therapy in PTEN-null prostate cancer by intermittent anti-PI3Kalpha/beta/delta treatment. _Nat. Commun._ 13, 182 (2022). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar


  * Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. _Genome Biol._ 18, 220 (2017). Article  PubMed  PubMed Central  Google


Scholar  * Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. _Cancer Cell_ 18, 11–22 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Crowley, M. J.


P. et al. Tumor-intrinsic IRE1alpha signaling controls protective immunity in lung cancer. _Nat. Commun._ 14, 120 (2023). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  *


Mandula, J. K. et al. Ablation of the endoplasmic reticulum stress kinase PERK induces paraptosis and type I interferon to promote anti-tumor T cell responses. _Cancer Cell_ 40,


1145–1160.e1149 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Raymundo, D. P. et al. Pharmacological targeting of IRE1 in cancer. _Trends Cancer_ 6, 1018–1030 (2020).


Article  CAS  PubMed  Google Scholar  * Lhomond, S. et al. Dual IRE1 RNase functions dictate glioblastoma development. _EMBO Mol. Med._ 10. https://doi.org/10.15252/emmm.201707929 (2018). *


Logue, S. E. et al. Inhibition of IRE1 RNase activity modulates the tumor cell secretome and enhances response to chemotherapy. _Nat. Commun._ 9, 3267 (2018). Article  ADS  PubMed  PubMed


Central  Google Scholar  * Zhao, N. et al. Pharmacological targeting of MYC-regulated IRE1/XBP1 pathway suppresses MYC-driven breast cancer. _J. Clin. Invest_ 128, 1283–1299 (2018). Article


  PubMed  PubMed Central  Google Scholar  * Pestka, S., Krause, C. D. & Walter, M. R. Interferons, interferon-like cytokines, and their receptors. _Immunol. Rev._ 202, 8–32 (2004).


Article  CAS  PubMed  Google Scholar  * Zitvogel, L., Galluzzi, L., Kepp, O., Smyth, M. J. & Kroemer, G. Type I interferons in anticancer immunity. _Nat. Rev. Immunol._ 15, 405–414


(2015). Article  CAS  PubMed  Google Scholar  * Fenton, S. E., Saleiro, D. & Platanias, L. C. Type I and II interferons in the anti-tumor immune response. _Cancers (Basel)_ 13.


https://doi.org/10.3390/cancers13051037 (2021). * Diamond, M. S. et al. Type I interferon is selectively required by dendritic cells for immune rejection of tumors. _J. Exp. Med_ 208,


1989–2003 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Fuertes, M. B. et al. Host type I IFN signals are required for antitumor CD8+ T cell responses through CD8alpha+


dendritic cells. _J. Exp. Med_ 208, 2005–2016 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Strasner, A. & Karin, M. Immune infiltration and prostate cancer. _Front


Oncol._ 5, 128 (2015). Article  PubMed  PubMed Central  Google Scholar  * Ebelt, K. et al. Prostate cancer lesions are surrounded by FOXP3+, PD-1+ and B7-H1+ lymphocyte clusters. _Eur. J.


Cancer_ 45, 1664–1672 (2009). Article  CAS  PubMed  Google Scholar  * Sfanos, K. S. et al. Human prostate-infiltrating CD8+ T lymphocytes are oligoclonal and PD-1+. _Prostate_ 69, 1694–1703


(2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Valdman, A. et al. Distribution of Foxp3-, CD4- and CD8-positive lymphocytic cells in benign and malignant prostate tissue.


_APMIS_ 118, 360–365 (2010). Article  PubMed  Google Scholar  * Hirz, T. et al. Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial


transcriptomic analyses. _Nat. Commun._ 14, 663 (2023). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Blank, C. U. et al. Defining ‘T cell exhaustion. _Nat. Rev. Immunol._


19, 665–674 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Luke, J. J. et al. The PD-1- and LAG-3-targeting bispecific molecule tebotelimab in solid tumors and hematologic


cancers: a phase 1 trial. _Nat. Med_ 29, 2814–2824 (2023). Article  CAS  PubMed  PubMed Central  Google Scholar  * Clancy-Thompson, E. et al. 461Generation of AZD7789, a novel PD-1 and TIM-3


targeting bispecific antibody, which binds to a differentiated epitope of TIM-3. _J. Immunother. Cancer_ 10, A481–A481 (2022). Google Scholar  * Liu, L. et al. Ablation of ERO1A induces


lethal endoplasmic reticulum stress responses and immunogenic cell death to activate anti-tumor immunity. _Cell Rep. Med_ 4, 101206 (2023). Article  CAS  PubMed  PubMed Central  Google


Scholar  * Guttman, O. et al. Antigen-derived peptides engage the ER stress sensor IRE1alpha to curb dendritic cell cross-presentation. _J. Cell Biol._ 221,


https://doi.org/10.1083/jcb.202111068 (2022). * Lin, J. et al. Targeting the IRE1alpha/XBP1s pathway suppresses CARM1-expressing ovarian cancer. _Nat. Commun._ 12, 5321 (2021). Article  ADS


  CAS  PubMed  PubMed Central  Google Scholar  * Carreras-Sureda, A. et al. Non-canonical function of IRE1alpha determines mitochondria-associated endoplasmic reticulum composition to


control calcium transfer and bioenergetics. _Nat. Cell Biol._ 21, 755–767 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Le Goupil, S., Laprade, H., Aubry, M. & Chevet,


E. Exploring the IRE1 interactome: from canonical signaling functions to unexpected roles. _J. Biol. Chem._ 300, 107169 (2024). Article  PubMed  PubMed Central  Google Scholar  * Maurel, M.,


Chevet, E., Tavernier, J. & Gerlo, S. Getting RIDD of RNA: IRE1 in cell fate regulation. _Trends Biochem Sci._ 39, 245–254 (2014). Article  CAS  PubMed  Google Scholar  * Quwaider, D.


et al. RNA sequencing identifies novel regulated IRE1-dependent decay targets that affect multiple myeloma survival and proliferation. _Exp. Hematol. Oncol._ 11, 18 (2022). Article  CAS 


PubMed  PubMed Central  Google Scholar  * Hinte, F., van Anken, E., Tirosh, B. & Brune, W. Repression of viral gene expression and replication by the unfolded protein response effector


XBP1u. _Elife_ 9. https://doi.org/10.7554/eLife.51804 (2020) * Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. _Nat. Protoc._ 8, 2281–2308 (2013). Article  CAS  PubMed 


PubMed Central  Google Scholar  * Xue, W. et al. CRISPR-mediated direct mutation of cancer genes in the mouse liver. _Nature_ 514, 380–384 (2014). Article  ADS  CAS  PubMed  PubMed Central 


Google Scholar  * Crowley, L. C., Marfell, B. J., Scott, A. P. & Waterhouse, N. J. Quantitation of Apoptosis and Necrosis by Annexin V Binding, Propidium Iodide Uptake, and Flow


Cytometry. _Cold Spring Harb. Protoc._ 2016. https://doi.org/10.1101/pdb.prot087288 (2016) * Jin, Y. et al. STAMP2 increases oxidative stress and is critical for prostate cancer. _EMBO Mol.


Med_ 7, 315–331 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cai, H. et al. CRISPR/Cas9 model of prostate cancer identifies Kmt2c deficiency as a metastatic driver by


Odam/Cabs1 gene cluster expression. _Nat. Commun._ 15, 2088 (2024). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Ewels, P. A. et al. The nf-core framework for


community-curated bioinformatics pipelines. _Nat. Biotechnol._ 38, 276–278 (2020). Article  CAS  PubMed  Google Scholar  * Love, M. I., Huber, W. & Anders, S. Moderated estimation of


fold change and dispersion for RNA-seq data with DESeq2. _Genome Biol._ 15, 550 (2014). Article  PubMed  PubMed Central  Google Scholar  * Subramanian, A. et al. Gene set enrichment


analysis: a knowledge-based approach for interpreting genome-wide expression profiles. _Proc. Natl Acad. Sci. USA_ 102, 15545–15550 (2005). Article  ADS  CAS  PubMed  PubMed Central  Google


Scholar  * Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. _BMC Bioinforma._ 14, 128 (2013). Article  Google Scholar  * Kuleshov, M. V. et


al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. _Nucleic Acids Res_ 44, W90–W97 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Xie, Z. et


al. Gene set knowledge discovery with enrichr. _Curr. Protoc._ 1, e90 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * Sikkeland, J. et al. STAMP2 suppresses autophagy in


prostate cancer cells by modulating the integrated stress response pathway. _Am. J. Cancer Res_ 12, 327–336 (2022). CAS  PubMed  PubMed Central  Google Scholar  * Hughes, C. S. et al.


Ultrasensitive proteome analysis using paramagnetic bead technology. _Mol. Syst. Biol._ 10, 757 (2014). Article  PubMed  Google Scholar  * Moggridge, S., Sorensen, P. H., Morin, G. B. &


Hughes, C. S. Extending the compatibility of the SP3 paramagnetic bead processing approach for proteomics. _J. Proteome Res_ 17, 1730–1740 (2018). Article  CAS  PubMed  Google Scholar  *


Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. _Nat. Protoc._ 10, 1567–1593


(2015). Article  CAS  PubMed  Google Scholar  * Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in


large proteomic data sets. _Mol. Cell Proteom._ 14, 2394–2404 (2015). Article  CAS  Google Scholar  * Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing


and microarray studies. _Nucleic Acids Res_ 43, e47 (2015). Article  PubMed  PubMed Central  Google Scholar  * Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A. & Vingron, M.


Variance stabilization applied to microarray data calibration and to the quantification of differential expression. _Bioinformatics_ 18, S96–S104 (2002). Article  PubMed  Google Scholar  *


Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. _Nat. Biotechnol._ 36,


411–420 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with harmony. _Nat. Methods_ 16,


1289–1296 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Alquicira-Hernandez, J. & Powell, J. E. Nebulosa recovers single-cell gene expression signals by kernel density


estimation. _Bioinformatics_ 37, 2485–2487 (2021). Article  CAS  PubMed  Google Scholar  * Gennady Korotkevich, V. S., Nikolay Budin, Boris Shpak, Maxim N. Artyomov, Alexey Sergushichev.


Fast gene set enrichment analysis. Preprint at _bioRxiv_ https://doi.org/10.1101/060012 (2021). * Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. _Nucleic Acids Res_


48, W509–W514 (2020). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the EMBL Proteomics Core Facility and NorSeq Sequencing Core for


proteomics and RNA-seq analysis, respectively. We thank Mehmet Ilyas Cosacak for his input on the scRNA-seq data analysis. FS was funded by the Norwegian Research Council (303353), Norwegian


Cancer Society (247110), Helse Sør-Øst and Anders Jahre fund (102583101, 10000). MLK was funded by the Norwegian Research Council, Helse Sør-Øst, and the University of Oslo through the


Center for Molecular Medicine Norway (187615), the Norwegian Research Council (313932), and the Norwegian Cancer Society (214871). MD, HZO, and SHYK were funded by a TerryFox New Frontiers


Program Project Grant (1109-UBC GR026025). MKT was funded by the Danish Cancer Society (311-A18039). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biosciences, University of


Oslo, Oslo, Norway Bilal Unal, Omer Faruk Kuzu, Yang Jin & Fahri Saatcioglu * Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway Bilal Unal, Omer Faruk


Kuzu, Wanja Kildal, Manohar Pradhan & Fahri Saatcioglu * Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway Daniel Osorio & Marieke


Lydia Kuijjer * Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, Canada Sonia H. Y. Kung, Htoo Zarni Oo & Mads Daugaard * Department


of Nuclear Medicine & PET Centre, Aarhus University Hospital, Aarhus, Denmark Mikkel Vendelbo * Orinove Inc., Newbury Park, California, USA John B. Patterson * Department of


Biomedicine, Aarhus University, Aarhus, Denmark Martin Kristian Thomsen * Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands Marieke Lydia Kuijjer * Leiden


Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands Marieke Lydia Kuijjer Authors * Bilal Unal View author publications You can also search for this


author inPubMed Google Scholar * Omer Faruk Kuzu View author publications You can also search for this author inPubMed Google Scholar * Yang Jin View author publications You can also search


for this author inPubMed Google Scholar * Daniel Osorio View author publications You can also search for this author inPubMed Google Scholar * Wanja Kildal View author publications You can


also search for this author inPubMed Google Scholar * Manohar Pradhan View author publications You can also search for this author inPubMed Google Scholar * Sonia H. Y. Kung View author


publications You can also search for this author inPubMed Google Scholar * Htoo Zarni Oo View author publications You can also search for this author inPubMed Google Scholar * Mads Daugaard


View author publications You can also search for this author inPubMed Google Scholar * Mikkel Vendelbo View author publications You can also search for this author inPubMed Google Scholar *


John B. Patterson View author publications You can also search for this author inPubMed Google Scholar * Martin Kristian Thomsen View author publications You can also search for this author


inPubMed Google Scholar * Marieke Lydia Kuijjer View author publications You can also search for this author inPubMed Google Scholar * Fahri Saatcioglu View author publications You can also


search for this author inPubMed Google Scholar CONTRIBUTIONS B.U. and F.S. conceived the study, designed the experiments, and wrote the manuscript. F.S. supervised the study. B.U. performed


the experiments with the supervision of F.S. Except for scRNA-seq analysis, O.F.K. performed all the other RNA-seq analyses and also generated ectopic XBP1s expressing cell lines. Y.J. and


O.F.K. helped with animal experiments. D.O. and M.L.K. analyzed the scRNA-seq data. W.K., M.P., S.H.Y.K., H.Z.O., and M.D. conducted and analyzed IHC analysis of PCa patient samples. M.K.T.


performed orthotopic PCa model experiment and obtained the MRI images with the help of M.V. J.B.P. provided critical reagents. All authors revised the manuscript. CORRESPONDING AUTHOR


Correspondence to Fahri Saatcioglu. ETHICS DECLARATIONS COMPETING INTERESTS JBP is employee and shareholder of Fosun Orinove. The remaining authors declare no competing interests. PEER


REVIEW PEER REVIEW INFORMATION _Nature Communications_ thanks Eric Chevet, John Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer


review file is available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION PEER REVIEW FILE DESCRIPTION OF ADDITIONAL SUPPLEMENTARY FILES SUPPLEMENTARY DATA 1 SUPPLEMENTARY DATA 2 SUPPLEMENTARY DATA 3


SUPPLEMENTARY DATA 4 REPORTING SUMMARY SOURCE DATA SOURCE DATA RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives


4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original


author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted


material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise


in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the


permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and


permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Unal, B., Kuzu, O.F., Jin, Y. _et al._ Targeting IRE1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate


cancer. _Nat Commun_ 15, 8895 (2024). https://doi.org/10.1038/s41467-024-53039-1 Download citation * Received: 20 July 2023 * Accepted: 30 September 2024 * Published: 15 October 2024 * DOI:


https://doi.org/10.1038/s41467-024-53039-1 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not


currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative