Targeting the mevalonate pathway potentiates NUAK1 inhibition-induced immunogenic cell death and antitumor immunity

Liming Gui; Kaiwen Chen; Jingjing Yan; Ping Chen; Wei-Qiang Gao; Bin Ma

doi:10.1016/j.xcrm.2024.101913

. 2025 Jan 16;6(2):101913. doi: 10.1016/j.xcrm.2024.101913

Targeting the mevalonate pathway potentiates NUAK1 inhibition-induced immunogenic cell death and antitumor immunity

Liming Gui ^1,^2,⁴, Kaiwen Chen ^1,^2,⁴, Jingjing Yan ^1,^2,³, Ping Chen ^1,², Wei-Qiang Gao ^1,², Bin Ma ^1,^2,^5,^∗

PMCID: PMC11866496 PMID: 39824180

Summary

The induction of immunogenic cell death (ICD) impedes tumor progression via both tumor cell-intrinsic and -extrinsic mechanisms, representing a robust therapeutic strategy. However, ICD-targeted therapy remains to be explored and optimized. Through kinome-wide CRISPR-Cas9 screen, NUAK family SNF1-like kinase 1 (NUAK1) is identified as a potential target. The ICD-provoking effect of NUAK1 inhibition depends on the production of reactive oxygen species (ROS), consequent to the downregulation of nuclear factor erythroid 2-related factor 2 (NRF2)-mediated antioxidant gene expression. Moreover, the mevalonate pathway/cholesterol biosynthesis, activated by spliced form of X-box binding protein 1 (XBP1s) downstream of ICD-induced endoplasmic reticulum (ER) stress, functions as a negative feedback mechanism. Targeting the mevalonate pathway with CRISPR knockout or the 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibitor simvastatin amplifies NUAK1 inhibition-mediated ICD and antitumor activity, while cholesterol dampens ROS and ICD, and therefore also dampens tumor suppression. The combination of NUAK1 inhibitor and statin enhances the efficacy of anti-PD-1 therapy. Collectively, our study unveils the promise of blocking the mevalonate-cholesterol pathway in conjunction with ICD-targeted immunotherapy.

Keywords: immunogenic cell death, ICD, the mevalonate pathway, cholesterol, reactive oxygen species, ROS, endoplasmic reticulum stress, NUAK1, XBP1

Graphical abstract

Highlights

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NUAK1 inhibition induces tumor immunogenic cell death (ICD)
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The XBP1s-activated mevalonate pathway is a negative feedback mechanism for ICD
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Cholesterol attenuates ICD by reducing reactive oxidative species
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Blockade of NUAK1 and the mevalonate pathway boosts antitumor immunity

Gui et al. demonstrate that inhibiting NUAK1 triggers immunogenic cell death in tumors and activates the mevalonate-cholesterol pathway as a compensatory mechanism. Suppressing cholesterol synthesis amplifies the immunogenic cell death caused by NUAK1 inhibition. Furthermore, the synergistic use of a NUAK1 inhibitor with statins increases tumor sensitivity to anti-PD-1 therapy.

Introduction

Cancer cells evade immunosurveillance or immunotherapy by suppressing immune response through various mechanisms. Immunogenic cell death (ICD) triggers a series of inflammatory events characterized by the release of cell contents and immunomodulatory molecules including damage-associated molecular patterns (DAMPs) and cytokines,¹^,²^,³ which are crucial for activating antigen-presenting cells (APCs) and T cell responses. Unlike checkpoint inhibition, ICD can initiate antitumor immunity in “cold” tumors lacking immune infiltrates, making it a promising strategy for cancer immunotherapy. ICD can be triggered by multiple approaches, such as chemotherapy,⁴^,⁵ radiotherapy,⁶ oncolytic virus,⁷ and targeted therapy such as kinase inhibitors.⁸^,⁹^,¹⁰ Cyclin-dependent kinase (CDK) inhibitors, for example, have been shown to enhance the efficacy of anti-PD-1 antibodies by inducing ICD.⁹ However, a systemic evaluation of kinase targets, whose inhibition leads to ICD, is still lacking. Typical ICD-induced DAMPs consist of endoplasmic reticulum (ER) chaperone calreticulin (CALR) exposed on the cell surface,¹¹ released ATP,¹² and the non-histone chromatin-binding high-molecular group B1 (HMGB1).¹³ At the early stage of ICD, CALRs translocate onto the membrane of dying cells, serving as an “eat-me” signal for dendritic cells (DCs) and a crucial mediator of tumor immunogenicity.¹¹^,¹⁴ The externalized CALR (ecto-CALR) is therefore a hallmark of ICD and a valuable readout for high-throughput CRISPR-Cas9 screens to identify ICD-inducing targets.

NUAK family SNF1-like kinase 1 (NUAK1), a serine/threonine protein kinase of the AMP-activated protein kinase family, is overexpressed in various cancers and associated with poor prognosis.¹⁵^,¹⁶^,¹⁷^,¹⁸^,¹⁹ It protects cancer cells from nutrient deficiency-induced apoptosis²⁰ and from oxidative stress by increasing the nuclear translocation of antioxidant regulatory molecule nuclear factor erythroid 2-related factor 2 (NRF2).¹⁹ Therefore, NUAK1 is an attractive therapeutic target for cancer, and efforts have been made to develop its inhibitors.²¹^,²²^,²³

The mevalonate pathway, which utilizes acetyl coenzyme A to produce cholesterol and isoprenoid lipids, has been shown to promote tumor development and progression through various tumor cell-intrinsic mechanisms, such as increased proliferation and metastasis, and the maintenance of tumor stem cells.²⁴^,²⁵ In particular, statins, which are inhibitors of this pathway and the most commonly prescribed drugs for hypercholesterolemia, have been tested clinically for cancer treatment. The mevalonate pathway and cholesterol also influence the function of different immune cells in the tumor microenvironment and affect the efficacy of immunotherapy.²⁶^,²⁷^,²⁸^,²⁹^,³⁰ High cholesterol in the tumor microenvironment strengthens the immunosuppressive activity of myeloid cells²⁶ and induces CD8⁺ T cell exhaustion.²⁷ In contrast, cholesterol deficiency leads to decreased proliferation and increased apoptosis of T cells.³⁰ Therefore, the role of the mevalonate pathway and cholesterol in tumor immunity remains controversial and needs further investigation.

Here, we identify NUAK1 as a target for ICD-based therapy using a CRISPR-Cas9 screen and further reveal the mevalonate-cholesterol pathway as negative feedback for ICD induced by NUAK1 blockade. Targeting the mevalonate pathway and ICD results in enhanced tumor control and improves the efficacy of other immunotherapies.

Results

NUAK1 inhibition elicits immunogenic death of tumor cells

To identify kinases critical for the regulation of tumor ICD, we conducted a CRISPR-Cas9 knockout (KO) screen in the MC38 mouse colorectal carcinoma cell line, using a lentiviral library targeting 713 murine kinase genes with 2,852 single-guide (sg) RNAs.³¹ CALR translocated to the cell surface served as the ICD marker. Cells displaying ecto-CALR and negative for propidium iodide were isolated via flow cytometry and subjected to sequencing to analyze effective gene perturbations (Figure 1A). The most enriched genes included Nuak1, Ern2, Pgk1, Cdk18, and several mitogen-activated protein kinase (MAPK) family members (Figure 1B). Since previous studies have suggested the ICD-inducing potentials of Ern2, Pgk1, CDKs, and MAPKs,⁹^,³²^,³³^,³⁴^,³⁵ we focused on Nuak1 due to its top rank and lack of research on its impact on tumor immunogenicity. Elevated NUAK1 mRNA levels were found in colorectal adenocarcinoma compared to normal tissues (Figure 1C), and high expression correlated with advanced disease and poorer prognosis (Figures S1A and S1B). Analysis of the Tumor Immune Dysfunction and Exclusion score revealed a more immunosuppressive environment in tumors with high NUAK1 expression (Figure S1C). These data point out a pro-tumoral role of NUAK1.

Next, we evaluated NUAK1 expression in various mouse and human tumor cell lines at the protein level (Figures S1D and S1E). To validate the involvement of NUAK1 in tumor ICD, we first introduced NUAK1 KO to mouse colorectal cancer cell line MC38 and mouse esophageal cancer cell line AKR, both characterized by high NUAK1 expression. This led to a significant elevation in ecto-CALR expression (Figures 1D and S1F), and similar results were observed in the human colorectal cancer cell line HCT116 (Figures S1G and S1H). We further exploited a specific NUAK1 inhibitor HTH-01-015.²² As expected, HTH-01-015 treatment dose dependently increased ecto-CALR expression and apoptotic rates in MC38 and AKR cell lines (Figures 1E and 1F). Immunofluorescence assay unraveled an overall increase in cellular CALR upon NUAK1 inhibition (Figure 1G). In addition, HTH-01-015-treated tumor cells exhibited enhanced ATP release and HMGB1 secretion, confirming a pronounced ICD induction (Figures 1H–1J). In contrast, the CT26 tumor cell line with low NUAK1 expression did not show significant ICD induction following HTH-01-015 treatment (Figure S1I). Consistently, HTH-01-015 also provoked the ICD of human colorectal cancer cell lines HCT116 and SW480 and human esophageal cancer cell line TE-1 (Figures S1J and S1K). A critical aspect of ICD is the emission of “find-me” and “eat-me” signals by dying tumor cells, facilitating their recognition and phagocytosis by APCs. To validate this point, we co-cultured bone-marrow-derived DCs (BMDCs) or macrophages with CFSE-labeled tumor cells. Pre-treatment of MC38 tumor cells with HTH-01-015 resulted in a significant enhancement in antigen uptake by BMDCs or macrophages (Figure 1K). The same phagocytosis-promoting effect of HTH-01-015 treatment was observed in human THP-1-derived macrophage-like cells co-cultured with human cancer cell lines HCT116 and SW480 (Figure S1L). To further verify the effect of NUAK1 inhibition-induced antigen-specific T cell response, BMDCs co-cultured with MC38 cells overexpressing ovalbumin (OVA) (MC38-OVA) were used to stimulate T cells from OT-1 mice bearing the T cell receptor specific for major histocompatibility complex (MHC)-I-presented OVA peptide. In line with the upregulated CD86 on BMDCs (Figure S1M), BMDCs co-cultured with HTH-01-015-pretreated tumor cells showed strong stimulatory effects on CD8⁺ OT-1 T cells as indicated by the increased cytotoxic maker CD107a and proliferative marker Ki67 (Figure 1L). These findings demonstrate the potential of NUAK1 inhibition to efficiently induce ICD in tumor cells, augment antigen uptake and presentation by APCs, and promote antigen-specific T cell response.

NUAK1 inhibition triggers ICD via ROS-induced ER stress

Previous research shows that NUAK1 shields tumors from oxidative stress by enhancing the nuclear translocation of the antioxidant master regulator NRF2.¹⁹ Moreover, ER stress can be provoked by reactive oxygen species (ROS) and is widely recognized as an initiator of ICD.¹ Thus, it is plausible to hypothesize that NUAK1 inhibition could lead to an accumulation of ROS in tumor cells, thereby inducing ER stress and subsequent ICD. Indeed, elevated ROS levels were found in both Nuak1/NUAK1-KO mouse and human tumor cell lines, as well as in tumor cells treated with the NUAK1 inhibitor HTH-01-015 (Figures 2A and S2A–S2C). This ROS accumulation could be effectively dampened by the ROS scavenger Trolox, which concurrently inhibited tumor ICD (Figures 2B and 2C), indicating that NUAK1 inhibition-triggered ICD depended on ROS production. Similarly, tert-butyl hydroperoxide (tBHP), a known ROS inducer, elevated ROS levels and facilitated ICD in tumor cells (Figures S2D and S2E). It has been demonstrated that phosphorylation of MYPT1 at S445 by NUAK1 leads to the activation of NRF2-mediated antioxidant program.¹⁹ Western blot analysis showed that HTH-01-015-treated MC38 and AKR cells exhibited reduced phosphorylation of MYPT1 at S445 and reduced nuclear translocation of NRF2 (Figures 2D and 2E). Immunofluorescence images further revealed a reduction of hydrogen peroxide (H₂O₂)-induced NRF2 by NUAK1 inhibition (Figure 2F). The inhibitory effect on MYPT1 phosphorylation was also observed in Nuak1-KO mouse tumor cells (Figure S2F) and HTH-01-015-treated human cancer cell line HCT116 (Figure S2G). Consequently, the expression of established NRF2-regulated antioxidant genes such as Gclc, Gclm, Sod1, Gpx1, and Gpx8 was decreased in HTH-01-015-treated mouse tumor cells, while a decrease of Gclc and Gclm was also observed in Nuak1-KO tumor cells (Figures 2G and S2H).³⁶ Besides, HTH-01-015 downregulated GCLC, GCLM, and SLC7A11 in human HCT116 tumor cells (Figure S2I). Accordingly, mRNA expression of NUAK1 was found to be positively correlated with various antioxidant genes in human tumor samples (Figure S2J), highlighting the role of NUAK1 in antioxidant defense.

Evaluation of ER stress markers upon HTH-01-015 treatment unveiled an upsurge in phosphorylated IRE1α, PERK, and eIF2α and upregulation of the downstream targets including spliced form of X-box binding protein 1 (XBP1s), ATF4, and ATF4-regulated cell death marker CHOP in both mouse and human tumor cell lines (Figures 2H and S2K). In addition, electron microscopy provided direct visual evidence of ER stress that the lumen was enlarged in tumor cells treated with HTH-01-015 (Figure 2I). Inhibition of ER stress using tauroursodeoxycholic acid abolished HTH-01-015-induced ICD indicating the dependency on ER stress (Figure S2L).³⁷ It has been established that intracellular ROS accumulation can provoke protein-folding stress by altering ER-resident calcium channels and promoting lipid peroxidation, eventually leading to ER proteostasis perturbation and ER stress.³⁸ Treatment with antioxidant Trolox also attenuated the basal and HTH-01-015-activated ER stress markers (Figure S2M), in line with the observation in ecto-CALR expression (Figure 2C), confirming the critical role of ROS in the induction of ER stress. These findings depict a mechanistic pathway whereby NUAK1 inhibition orchestrates oxidative and ER stress responses, culminating in the induction of ICD.

NUAK1 blockade suppresses tumor growth by promoting antitumor immunity

To investigate the impact of NUAK1 on tumor progression, Nuak1-KO (sgNuak1) MC38 and AKR cell lines were employed to establish subcutaneous tumors. sgNuak1 tumors exhibited a slower growth rate compared to control sgLacZ tumors in both tumor types (Figure 3A), in agreement with the finding that Nuak1 overexpression accelerated the growth of CT26 tumors (Figures S3A–S3D). To validate the potential of pharmacological inhibition of NUAK1 for cancer treatment, HTH-01-015 was administered intratumorally to MC38 and AKR subcutaneous tumor models every other day. This treatment significantly reduced tumor growth (Figure 3B). Notably, the depletion of CD8⁺ T cells using anti-CD8α antibody abolished the antitumor effect of HTH-01-015 (Figure 3C), demonstrating the dependence of NUAK1 blockade-mediated tumor suppression on T cell immunity.

The aforementioned results imply that the antitumor immunity elicited by ICD is essential for tumor suppression. To comprehensively evaluate the effect of NUAK1 inhibition on the immune microenvironment, we performed single-cell RNA sequencing (scRNA-seq) analysis of tumor-infiltrating CD45⁺ cells in the MC38 model (Figures 3D–3H and S3E). The ratio and activity of CD8⁺ T and natural killer (NK) cells were enhanced by HTH treatment (Figure 3D). Functional markers like costimulatory ligand Tnfsf9, MHC-II component H2-Ab1, and inflammatory cytokine Il1b in DCs were also upregulated by HTH-01-015, while the pro-tumor chemokine Ccl6 was downregulated,³⁹ indicating enhanced antigen presentation and antitumor activity (Figure 3E). Moreover, macrophages shifted to an M1 phenotype (Figures 3F and 3G). CD8⁺ T cell activity was strengthened, as indicated by increased expression of Mki67, Ifng, and Prf1 (Figure 3H). Together, these results indicated that NUAK1 inhibition transformed the tumor immune microenvironment into an active antitumor state. Fluorescence-activated cell sorting (FACS) analysis of the immune landscape in Nuak1-KO tumors revealed a pronounced infiltration and activation of CD8⁺ T cells, characterized by increased expression of the activation marker CD69 (Figure 3I), cytotoxic molecule granzyme B (GzmB), and antitumor cytokine interferon gamma (IFNγ) (Figure 3J). Alongside T cell activation, tumor-intrinsic Nuak1 deficiency upregulated DCs and their costimulatory marker CD86 (Figure 3I), suggesting an enhanced antigen presentation. Treatment with HTH-01-015 boosted CD8⁺ T cell activation in tumors as indicated by elevated CD69, and PD-1, which also served as an exhaustion marker (Figure 3I). This observation provides a rationale for combining HTH-01-015 with PD-1 blockade to achieve better tumor control. Immunohistochemical analysis of HTH-01-015-treated tumors showed increased oxidative damage marker 8-oxo-dG and ICD marker HMGB1 (Figure S3F), consistent with the in vitro effects of HTH-01-015 in provoking ROS and ICD. Moreover, ratios of CD4⁺ T and CD8⁺ T cells in the peripheral blood of treated mice were elevated, indicating a boost of systemic antitumor immunity (Figure S3H). To further verify the in vivo role of antigen presentation on HTH-mediated tumor suppression, CD11c⁺ APCs were depleted by administering diphtheria toxin to CD11c-DTR mice. The antitumor activity of HTH-01-015 against MC38 tumors was completely abolished by the loss of APCs (Figure S3G), pinpointing the essential role of the link between APCs and cytotoxic effects of T cells in NUAK1 inhibition-induced tumor suppression. These data demonstrate that the in vivo antitumor effects of NUAK1 blockade rely on the ICD-elicited antigen presentation and T cell response.

To test whether NUAK1 blockade could improve the efficacy of other immunotherapies, we employed agonistic antibodies for costimulatory receptors CD137 and GITR in conjunction with HTH-01-015 treatment. Agonism of these costimulatory receptors has been shown to enhance the survival and antitumor activities of tumor-infiltrating lymphocytes, representing a potentially effective strategy to overcome the tumor-induced immunosuppression for ICD-mobilized lymphocytes.⁴⁰^,⁴¹ Apparently, combination therapy drastically inhibited tumor growth and abolished tumor bioluminescence signals in some of the treated mice (Figures 3K and 3L). Next, we administered OT-1 cells and/or HTH-01-015 to mice bearing MC38-OVA tumors to explore the synergistic effect of NUAK1 inhibition and adoptive cell therapy. HTH-01-015 significantly improved the efficacy of the adoptive T cell therapy (Figure S3I). Lastly, we assessed the impact of NUAK1 inhibition on immune cells by evaluating NUAK1 expression in mouse and human tumor-infiltrating immune cells using our scRNA-seq data and public databases (Figures S3J and S3K). NUAK1 was found to be hardly expressed in immune cells, excluding possible detrimental effects on immune cells when the NUAK1 inhibitor is administered either locally or systemically. Overall, these results support the promising therapeutic potential of targeting NUAK1 either alone or in combination with other immunotherapies.

NUAK1 blockade leads to hyperactivation of the mevalonate pathway and cholesterol biosynthesis

To clarify the mechanisms behind NUAK1 blockade and its ICD-inducing activity, we performed pathway analysis of tumor cells treated with HTH-01-015 and uncovered an enrichment in immune response pathways such as cytokine signaling (Figures 4A and S4A), aligning with prior findings. Intriguingly, we also observed a marked activation of cholesterol biosynthesis, primarily through the mevalonate pathway, in HTH-01-015-treated tumor cells (Figure 4A). Key enzymes involved in cholesterol biosynthesis, such as Hmgcs1, Hmgcr, Acat2, Fdft1, and Sqle, exhibited significant upregulation at mRNA levels (Figure 4B), further validated by qPCR in MC38 and AKR cells (Figure 4C). In contrast, enzymes from the mevalonate pathway, but not directly involved in cholesterol biosynthesis, showed little to no significant changes (Figure S4B). To ascertain the impact of NUAK1 inhibition on cholesterol accumulation in tumor cells, Filipin III staining, analyzed by immunofluorescence and FACS, verified an increased amount of cholesterol in HTH-01-015-treated cells (Figures 4D and 4E). Mass spectrometry-based analysis confirmed increased cholesterol and various lipid metabolites of the mevalonate pathway by HTH-01-015 treatment (Figures 4F and 4G). Consistent effects of NUAK1 blockade on the expression of key enzyme genes were observed in human tumor cells from a re-analysis of RNA sequencing data of the previous study by Port et al. (Figure S4C)¹⁹ and our qPCR results (Figure S4D). Subsequent HTH-01-015-induced cholesterol accumulation was found in both HCT116 and SW480 (Figures S4E and S4F). Uptake of exogenous cholesterol from the surroundings represents an alternative way for cancer cells to facilitate their proliferation and survive stress.⁴² Therefore, we assessed the role of cholesterol uptake by measuring cholesterol content in HTH-01-015-treated cells under serum-free conditions, where the culture media contained no cholesterol. HTH-01-015 still raised the cholesterol content in these tumor cells (Figure S4G), suggesting that the enhanced biosynthesis is sufficient to elevate cellular cholesterol and plays an essential role downstream of NUAK1 inhibition. Analysis of clinical data from The Cancer Genome Atlas database revealed a negative correlation of NUAK1 expression with several key enzymes of the mevalonate pathway (Figure S4H), corroborating a positive effect of NUAK1 blockade on cholesterol biosynthesis.

Next, we sought to interpret the mechanism by which NUAK1 blockade upregulated cholesterol biosynthesis-related genes in tumor cells. The previous study by Yang et al. reported that the ER stress-activated XBP1s could activate genes involved in cholesterol biosynthesis in tumor cells.²⁶ Given our observation that XBP1s was activated by NUAK1 blockade, we reasoned that NUAK1 blockade-induced ER stress might trigger the alternative splicing of XBP1 into XBP1s, leading to upregulated expression of cholesterol biosynthesis-related genes. As expected, Xbp1 KO (sgXbp1) significantly abolished HTH-01-015-induced expression of crucial genes regulating cholesterol biosynthesis, including Hmgcs1, Hmgcr, and Sqle (Figures 4H and S4I–S4K), as well as diminished cholesterol accumulation in the tumor cells (Figure 4I). Our findings unveil a correlation between NUAK1 inhibition-induced ICD and cholesterol metabolism. A sharp elevation of cholesterol biosynthesis in human colorectal tumors compared to normal tissue by Reactome analysis and upregulated expression of key enzymes in human colon and rectal tumors (Figures S4L and S4M) prompted us to delineate the role of increased cholesterol biosynthesis in tumor ICD.

Targeting the mevalonate pathway sensitizes tumor cells to NUAK1 inhibition-induced ICD

Although the function of the mevalonate pathway and cholesterol in tumor growth and immunity has been reported, their impact on tumor ICD remains unclear. Then, we introduced KO of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), a crucial rate-limiting enzyme gene in the mevalonate pathway and cholesterol biosynthesis (Figure S5A), as well as XBP1, which controls the expression of HMGCR, in MC38 tumor cells. The KO of Hmgcr (sgHmgcr) or Xbp1 (sgXbp1) significantly intensified the ICD phenotype induced by HTH-01-015 (Figures 5A and S5B). Since statins are clinically used to lower cholesterol, we exploited the HMGCR inhibitor simvastatin. The addition of simvastatin efficiently eliminated HTH-01-015-induced cholesterol accumulation, although it did not alter the basal level (Figure S5C). This might be because a substantial portion of the basal cholesterol level detected is the constitutive structural component such as the membrane lipid composition, while HTH-01-015-induced cholesterol represents the MVA pathway-mediated newly synthesized fraction. Consequently, simvastatin amplified HTH-01-015-induced ICD (Figures 5B and S5D), accompanied by elevated ROS levels in mouse tumor cells (Figure 5C). The same effect of simvastatin on HTH-01-015-induced ecto-CALR was found in human tumor cells (Figure S5E). Simvastatin alone significantly impacted ecto-CALR and ROS in AKR but not MC38 cells, probably due to their different sensitivities to statins.⁴³ To explore the involvement of other key enzymes from the mevalonate pathway, we evaluated the functions of GGPS1, SQLE, and FDFT1 (Figure S5A). KO of these genes (sgGgps1, sgFdft1, and sgSqle) significantly increased HTH-01-015-induced ecto-CALR and ROS (Figures 5D and 5E), which aligns with their negative effect on HTH-01-015-induced cholesterol (Figures 5F, S5F, and S5G). Intriguingly, GGPS1 might be involved in cholesterol biosynthesis besides its well-established regulatory role in GGPP production (Figures 5F and S5A), which requires future investigation. These results pinpoint an inhibitory role of the mevalonate pathway on ICD and unravel a cholesterol-mediated negative feedback mechanism downstream of NUAK1 inhibition.

In agreement with in vitro findings, Hmgcr-KO (sgHmgcr) subcutaneous tumors displayed higher sensitivity to NUAK1 blockade in vivo (Figure 5G). Inhibition of HMGCR by simvastatin also potentiated the tumor-suppressive effects of HTH-01-015 in both MC38 and AKR tumor models (Figures 5H and 5I). While simvastatin alone showed a tendency to inhibit AKR tumor growth, Hmgcr-KO showed a trending but not statistically significant effect, and simvastatin alone did not exhibit any effect on MC38 tumor growth, suggesting that MC38 tumors are less sensitive to blockade of basic cholesterol biosynthesis as compared to AKR tumors in vivo. HTH-01-015, simvastatin, or a combination of both failed to exert any tumor-suppressing activity in nude mice (Figure S5H), highlighting the dependency of these treatments on T cell immunity.

To elucidate the impact of blockade of NUAK1 and the mevalonate pathway on the immune microenvironment, we performed FACS analysis in the MC38 tumor model. Hmgcr-KO increased the HTH-01-105-induced ratio of DCs, as well as both basal and HTH-01-105-induced ratios of CD8⁺ T cells (Figure 5J), and improved the Ki67⁺ expression in CD8⁺ T cells from the HTH-01-105-treated group (Figure 5K). NUAK1 inhibition promoted the percentage and activity of CD8⁺ T cells, which is in line with the scRNA-seq results in the MC38 model (Figures 3D and 3H), and exhibited an additive effect on Hmgcr-KO. Additionally, immunohistochemistry analysis of the AKR tumors showed that HMGB1 expression was upregulated by HTH-01-015 and further promoted by additional simvastatin treatment indicating an enhancement of NUAK1 inhibition-induced ICD by the blockade of mevalonate pathway in vivo (Figure S5I). Subsequent FACS analysis of DCs revealed an increase in both cell ratio and activity (Figure 5L), suggesting a promotion in tumor antigen presentation. As a result, cytotoxic and proliferative CD8⁺ T cells from tumor-draining lymph nodes were substantially boosted by HTH-01-015, simvastatin, and the combination therapy, indicated by the upregulated activation markers GzmB and IFNγ and the proliferation marker Ki67 (Figure 5M). Accordingly, the combination treatment also led to the most prominent increase in tumor-infiltrating CD8⁺ T cells with enhanced effector function and proliferation (Figure 5N). Systemic blockade of the mevalonate pathway by simvastatin exerted a positive effect on tumor-infiltrating CD8⁺ T cells (Figure 5N), resembling the action of Hmgcr-KO in tumor cells in the MC38 model (Figures 5J and 5K), although simvastatin may directly target CD8⁺ T cells or other immune cells in the tumor microenvironment. Moreover, there was a significant elevation in the infiltration of conventional CD4⁺ T cells by all treatments, while the regulatory T (Treg) cells were only slightly upregulated by HTH-01-015 without changes in Tim3 expression that is linked to Treg cell immunosuppressive activity (Figure 5O).⁴⁴ Notably, higher ratios of CD8⁺ T cells to Treg cells by HTH-01-015 and the combination therapy were observed in AKR tumors, pointing to an overall immunostimulatory effect of these treatments (Figure 5P). Collectively, these findings demonstrate that blocking the mevalonate pathway and cholesterol biosynthesis synergizes with NUAK1 inhibition to elicit stronger tumor ICD and antitumor immunity.

Cholesterol impairs NUAK1 inhibition-induced ICD and antitumor effect

We next sought to elucidate whether the mevalonate pathway metabolites, especially cholesterol, impacted ICD. Supplementing tumor cells with exogenous cholesterol effectively downregulated the HTH-01-015-triggered ecto-CALR in both mouse (Figures 6A and 6B) and human tumor cell lines (Figure S6A). Moreover, HTH-01-015-induced ROS was significantly decreased by cholesterol, though the basic ROS level was not affected possibly due to the complexity of ROS molecules and the fact that excessive cholesterol may not necessarily be required by tumor cells to maintain redox balance under the non-stressed basal condition (Figures 6C and S6B).⁴⁵ In contrast, GGPP, a metabolite from another branch of the mevalonate pathway and regulated by the enzyme GGPS1, did not influence HTH-01-015-induced ICD (Figure 6D), suggesting that Ggps1-KO promoted ICD mainly through a reduction in cholesterol instead of GGPP (Figures 5D–5F). The tBHP-induced ecto-CALR and ROS were also attenuated by cholesterol (Figures S6C and S6D), supporting the broad potential of cholesterol to resist ROS and ICD. Notably, a series of established cholesterol oxidation products were increased in HTH-01-015-treated cells (Figures 4G and S6E),⁴⁶^,⁴⁷ implying an active oxidative reaction. To verify the direct antioxidant role of cholesterol, we tested the effect of cholesterol on two different types of ROS, including H₂O₂ and hydroxyl radical (HO⋅). As anticipated, cholesterol significantly neutralized both types of ROS in a dose-dependent manner in vitro (Figures S6F and S6G). These findings provide direct evidence that cholesterol may function as an antioxidant.

In the transmission electron microscopy analysis, tumor cells enriched with cholesterol exhibited significantly less ER lumen swelling upon HTH-01-015 treatment (Figure 6E), implying alleviated ER stress. These data demonstrate that elevated cholesterol in tumor cells impedes ROS accumulation and mitigates ER stress, thereby counteracting ICD.

To clarify the role of cholesterol in tumor growth in vivo, mice bearing MC38 and AKR tumors were subjected to a high-cholesterol diet (HCD), and they exhibited complete resistance to HTH-01-015 treatment (Figures 6F and 6G). In line with previous findings by Yang et al. that elevated cholesterol levels within the tumor microenvironment could activate myeloid-derived suppressor cells (MDSCs),²⁶ we observed that HCD augmented tumor-infiltrating MDSCs with high levels of the immunosuppressive PD-L1 (Figure 6H). Despite this, HTH-01-015 managed to reduce PD-L1 expression in MDSCs, albeit not sufficiently to diminish the adverse effects of HCD on tumor-infiltrating CD8⁺ T cells (Figure 6H) or consequently the tumor growth (Figures 6F and 6G). Thus, we found that NUAK1 inhibition during tumor ICD activates the mevalonate pathway and cholesterol biosynthesis, increasing cholesterol levels, which in turn negatively regulate ROS, ER stress, and ICD.

Dual inhibition of NUAK1 and the mevalonate pathway enhances immune checkpoint blockade efficacy

Currently, immune checkpoint inhibitors (immune checkpoint blockades [ICBs]) are the most commonly used cancer immunotherapy in clinical practice, but often exhibit limited efficacy in solid tumors due to their immunosuppressive microenvironment lacking immune effector cells. In the MC38 and AKR tumor models, we invoked tumor ICD by simultaneously inhibiting the mevalonate pathway and NUAK1, followed by the administration of PD-1 antibody. This synergistic strategy markedly suppressed tumor growth and improved tumor responsiveness to ICB therapy (Figures 7A–7C). FACS analyses of AKR tumor-infiltrating immune cells revealed a pronounced increase in CD8⁺ T cell infiltration and activation in the group receiving combination treatment (Figure 7D). While PD-1 antibody alone enhanced effector cell infiltration, it concurrently elevated Treg cell level, potentially undermining the antitumor efficacy (Figure 7E). Remarkably, the combined application of HTH-01-015 and simvastatin with PD-1 antibody effectively impeded the increase in Treg cells and encouraged the infiltration of Foxp3⁻ CD4⁺ T cells (Figure 7E). In addition, all treatments significantly boosted the accumulation and activation of NK cells within the tumors (Figure 7G). Eventually, we evaluated the combination treatments in the B16-F10 tumor model. Dual inhibition of NUAK1 and the mevalonate pathway exhibited additive effects on the induction of ICD (Figure S7A). HTH-01-015 and simvastatin exerted synergistic actions and further enhanced the antitumor activity of anti-PD-1 antibody for treating B16-F10 tumors with limited sensitivity to anti-PD-1 therapy as the AKR model (Figure S7B). These findings compellingly illustrate that the inhibition of NUAK1 and the mevalonate pathway can alter the immune landscape of solid tumors and augment the efficacy of ICB.

Discussion

Induction of ICD in tumors represents an attractive strategy to treat cancer and improve the efficacy of immunotherapy. This study focuses on identifying and characterizing kinase targets to induce ICD. Loss-of-function CRISPR screen and subsequent validation reveal that NUAK1 inhibition potently induces ICD in tumor cells. Consistent with an antioxidant role in colorectal cancer cells,¹⁹ we demonstrate that NUAK1 inhibition leads to the accumulation of ROS via reducing nuclear translocation of NRF2 and related antioxidant genes. Similar to other ICD inducers,⁴⁸^,⁴⁹ the ICD-inducing effect of NUAK1 inhibitor depends on ROS production as it can be abolished by antioxidant agents, manifesting the essential role of ROS in conducting ICD.¹ Despite the intricacies of ROS in tumor development and progression, cancer cells generally maintain higher ROS levels than normal tissues.⁵⁰ Consequently, they may develop various antioxidant mechanisms to withstand the detrimental effects of excessive ROS, with overexpression of NUAK1 potentially serving as one such defensive strategy.¹⁹^,⁵¹ Targeting the NUAK1-NRF2 pathway therefore sensitizes tumor cells to ROS-induced death. Although many anticancer treatments provoke different forms of cell death via excessive ROS production, not all of them are immunogenic.⁵² Thus, ER stress may serve as the second prerequisite for ICD and can be induced by NUAK1 inhibition. We found that the NUAK1 inhibitor activates the PERK-eIF2α pathway, which is instrumental in the translocation of the ER chaperone CALR from the ER to the plasma membrane, where it acts as an “eat-me” signal to promote phagocytosis by APCs.¹¹^,³⁸^,⁵³ Importantly, our results further show that the in vivo antitumor effect of the NUAK1 inhibitor was entirely abolished by the depletion of CD8⁺ T cells, suggesting that eliciting the adaptive immunity is more crucial than direct induction of cell death. This aligns with the understanding that the induction of ICD plays a significant role in the success of (immune)therapies for various cancers.⁵⁴

Notably, our study reveals that the IRE1α-XBP1 branch downstream of ER stress induced by NUAK1 inhibition upregulates the mevalonate pathway, especially the genes involved in cholesterol biosynthesis. In contrast to the PERK-CHOP arm, which plays a central role in the conduction of (immunogenic) cell death,³⁸^,⁵³^,⁵⁵ the IRE1α-XBP1 branch appears to serve as a negative feedback mechanism for ICD through the XBP1s-promoted cholesterol biosynthesis and the antioxidant effect of cholesterol. In agreement with our findings, previous research has shown that splicing of XBP1 contributes to resistance against ICD in colorectal cancer cells treated with a combination of chemotherapy and epidermal growth factor receptor (EGFR)-blocking antibodies, although the underlying mechanisms are still unclear.⁸ Our results suggest that XBP1s-upregulated cholesterol may antagonize chemotherapy and EGFR antibody-induced ICD. Interestingly, the activation of XBP1s-mediated negative feedback seems to precede the initiation of the CHOP-mediated apoptotic program (Figures 2H and S2K), implying a swift protective mechanism of the tumor cells. Instead of regulating cell death, tumor-intrinsic XBP1s has also been shown to elevate the production of cholesterol secreted to the microenvironment to activate MDSCs.²⁶ The effects of ER stress-activated XBP1s extend to immune cells as well. For example, persistent IRE1α-XBP1 activation in intertumoral DCs drives lipid droplet formation and inhibits their antigen presentation capacity,⁵⁶ while abrogating IRE1α-XBP1 activation in T cells enforces their antitumor activity.⁵⁷ These findings support a therapeutic potential of interfering with the IRE1α-XBP1 pathway.

Although inhibiting the GGPP branch of the mevalonate pathway has been shown to induce apoptosis or pyroptosis in certain types of cancer cells,⁵⁸^,⁵⁹ we find that cholesterol rather than GGPP is essential for counteracting NUAK1 inhibition-induced ICD and tumor repression. Despite the distinct roles of cholesterol biosynthesis and GGPP branches in tumor cell proliferation, survival, or other cellular activities, which may depend on cell type, stage of tumor progression, and/or the context of treatment, targeting the mevalonate pathway, especially at the upstream crucial enzyme such as HMGCR, which is required for both branches, represents a promising cancer treatment. In addition, the availability of clinically approved HMGCR inhibitors, e.g., statin drugs, makes this approach more attractive.

Tumor cells develop various resistant mechanisms to survive treatments exploiting ROS-induced cell death. Our finding that cholesterol negatively affects the activation of ICD by NUAK1 inhibitor suggests that increased intracellular cholesterol levels could serve as an additional resistance mechanism, countering ROS-induced cell death, likely due to cholesterol’s antioxidant properties (Figures 6C, S6E, and S6F).⁶⁰^,⁶¹^,⁶²^,⁶³ In particular, it will be beneficial to examine whether the XBP1-mevalonate pathway is activated by other clinically used ICD inducers such as the combination of chemotherapy and EGFR-blocking antibodies,⁸ to maximize their therapeutic efficacy by blocking this negative feedback.

While high cholesterol in tumor cells generally displays a pro-tumor function and the antitumor effect of cholesterol biosynthesis blockade has been well documented,⁴²^,⁶⁴ the roles of cholesterol in immune cells and tumor immunity are complicated and controversial. In contrast to its protective role in tumor cells against ROS-mediated ER stress and ICD, excess cholesterol has been shown to induce ER stress and cell death in macrophages.⁶⁵ Notably, more evidence supports a tumor-promoting effect of cholesterol and its metabolites due to the enhancement of immunosuppressive activity of MDSCs,²⁶ suppression of cytotoxic effector function of CD8⁺ T cells,²⁷^,⁶⁶ and impairment of DC function.⁶⁷ On the contrary, cholesterol deficiency has been shown to inhibit T cell proliferation and survival³⁰; high serum cholesterol level increases antitumor activity of NK cells and reduces liver tumor growth in mice.⁶⁸ These conflicting observations suggest that maintaining specific basal cholesterol levels is essential for the survival of various cell types. Differences in cholesterol levels across models or tissues, the varying sensitivities of immune cells and tumors to cholesterol changes, and the methods used to manipulate cholesterol can significantly impact treatment outcomes. However, our animal experiment data indicate that systemic administration of statin positively affects T cell immunity and improves the tumor-suppressing effect of NUAK1 inhibitor. Accordingly, we also show that an HCD dampens the antitumor efficacy of NUAK1 inhibitor. Therefore, targeting tumor mevalonate pathway using statins antagonizes the negative feedback downstream of NUAK1 inhibition and makes up a promising combination treatment with NUAK1 inhibitor. To minimize the potential adverse effects of statin on lymphocytes, we propose a combination of NUAK1 inhibitor, statins, and checkpoint inhibitors such as PD-1 antibody, which can reinforce the viability of lymphocytes. Indeed, a combination of all three drugs exhibits the most substantial antitumor efficacy in different tumor models. Given the challenges faced by clinically approved checkpoint inhibitors and statins for treating solid tumors, this combination therapy provides a new avenue. However, future preclinical evaluation of the efficacy and safety of NUAK1 inhibitors is warranted.

Limitations of the study

This study shows that NUAK1 inhibition provokes ICD by promoting ROS accumulation. As other forms of cell death such as necroptosis, pyroptosis, and ferroptosis also possess ICD features like DAMP release, the correlation of NUAK1 inhibition-induced ICD with these forms of cell death requires further investigation. While we mainly focus on delineating the roles of NUAK1 and cholesterol in ICD and tumor growth, whether other metabolites of the mevalonate pathway counteract ROS, and whether the XBP1s-cholesterol negative feedback loop is broadly functional downstream of other ICD inducers, can be explored in future studies. As ROS consists of many types of molecules besides H₂O₂ and HO⋅, comprehensive biochemical research will be helpful to interpret their responsiveness to cholesterol.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Dr. Bin Ma (bin.ma@outlook.com).

Materials availability

Further information and request for materials should be directed to and will be fulfilled by the lead contact.

Data and code availability

•
The raw sequencing data have been deposited in the Gene Expression Omnibus database and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
•
Original code has been deposited at GitHub and is publicly available as of the date of publication. The repository in GitHub is listed in the key resources table.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

This work was supported by grants from Ministry of Science and Technology of the People’s Republic of China (2023YFC3404100), National Natural Science Foundation of China (W2431055), Science and Technology Commission of Shanghai Municipality (20ZR1426500), Special Development Fund for Shanghai Zhangjiang National Independent Innovation Demonstration Zone (ZJ2021-ZD-007), Shanghai Jiao Tong University Trans-med Awards Research (STAR) Project (WF540162608), the 111 Project (B21024), and Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai.

Author contributions

L.G. and B.M. designed the experiments. L.G. and K.C. performed most of the experiments. J.Y. and P.C. helped with the in vitro experiments. W.-Q.G. helped with project design and data analysis. L.G., K.C., and B.M. analyzed the data and wrote the manuscript.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Alexa Fluor 647 Anti-Calreticulin	Abcam	Cat# ab196159, RRID:AB_2819061
Anti-IRE1 (phospho S724) antibody	Abcam	Cat# ab48187, RRID:AB_873899
8-Hydroxy-2′-deoxyguanosine Mouse pAb	Abcam	Cat# ab48508, RRID:AB_867461
HMGB1 Rabbit pAb	Abclonal	Cat# A2553, RRID:AB_2863012
BB700 Anti-Mouse CD86	BD Biosciences	Cat# 3247483, RRID:AB_2744454
BUV395 Anti-Mouse CD45	BD Biosciences	Cat# 564279, RRID:AB_2651134
BB700 Anti-Mouse CD279	BD Biosciences	Cat# 566832, RRID:AB_2869891
BV421 Anti-Mouse CD107a	BD Biosciences	Cat# 564347, RRID:AB_2738760
BB700 Anti-Mouse CD3e	BD Biosciences	Cat# 566494, RRID:AB_2744393
BV421 Anti-Mouse CD11c	BioLegend	Cat# 117330, RRID:AB_11219593
PE Anti-Mouse/Human CD11b	BioLegend	Cat# 101208, RRID:AB_312791
BV510 Anti-Mouse CD103	BioLegend	Cat# 121423, RRID:AB_2562713
BV510 Anti-Mouse CD8	BioLegend	Cat# 100752, RRID:AB_2563057
PE-Cy7 Anti-Mouse CD69	BioLegend	Cat# 104511, RRID:AB_493565
FITC Anti-Mouse IFN-γ	BioLegend	Cat# 505806, RRID:AB_315400
Alexa Fluor 488 Anti-Mouse Ki-67	BioLegend	Cat# 151204, RRID:AB_2566800
PE Anti-Mouse CD4	BioLegend	Cat# 100408, RRID:AB_312693
APC Anti-Mouse CD335	BioLegend	Cat# 137607, RRID:AB_10612749
PE-Cy7 Anti-Mouse CD274 (PD-L1)	BioLegend	Cat# 124314, RRID:AB_10643573
Biotin anti-mouse CD45 Antibody	BioLegend	Cat# 103104, RRID:AB_312969
InVivo MAb anti-mouse CD8α	BioXCell	Cat# BE0061, RRID:AB_3075397
InVivo MAb anti-mouse 4-1BB (CD137)	BioXCell	Cat# BE0169, RRID:AB_10949016
InVivo MAb anti-mouse GITR	BioXCell	Cat# BE0063, RRID:AB_1107688
InVivo Plus anti-mouse PD-1 (CD279)	BioXCell	Cat# BE0146, RRID:AB_3075399
InVivo MAb rat IgG2a isotype control	BioXCell	Cat# BE0089, RRID:AB_1107780
InVivo MAb rat IgG2b isotype control	BioXCell	Cat# BE0090, RRID:AB_1107769
ARK5 Antibody	Cell Signaling	Cat# 4458, RRID:AB_2155859
Nrf2 Rabbit mAb	Cell Signaling	Cat# 20733, RRID:AB_2934224
Phospho-PERK Rabbit mAb	Cell Signaling	Cat# 3179, RRID:AB_2095853
PERK Rabbit mAb	Cell Signaling	Cat# 5683, RRID:AB_10841299
CHOP Mouse mAb	Cell Signaling	Cat# 2895, RRID:AB_2089254
IRE1α Rabbit mAb	Cell Signaling	Cat# 3294, RRID:AB_823545
XBP-1s Rabbit mAb	Cell Signaling	Cat# 40435, RRID:AB_2891025
BiP Rabbit mAb	Cell Signaling	Cat# 3177, RRID:AB_2119845
CHOP Mouse mAb	Cell Signaling	Cat# 2895, RRID:AB_2089254
PDI Rabbit mAb	Cell Signaling	Cat# 3501, RRID:AB_2156433
Phospho-eIF2α (Ser51) Rabbit mAb	Cell Signaling	Cat# 3398, RRID:AB_2096481
eIF2α Antibody Rabbit pAb	Cell Signaling	Cat# 21271, RRID:AB_895266
ATF-4 (D4B8) Rabbit mAb	Cell Signaling	Cat# 11815, RRID:AB_2616025
Lamin A/C Rabbit pAb	Cell Signaling	Cat# 2032, RRID:AB_2136278
Anti-MYPT1 (phospho S445 aa 437–452) polyclonal antibody	Creative Diagnostics	Cat# CABT-BL6360; RRID:AB_3674399
BUV737 Anti-Mouse CD45	eBioscience	Cat# 367-0451-82, RRID:AB_2895963
PE Anti-Mouse Granzyme B	eBioscience	Cat# 12-8898-80, RRID:AB_10853811
PE Anti-Mouse CD366 (TIM3)	eBioscience	Cat# 12-5870-82, RRID:AB_465974
APC Anti-Mouse Foxp3	eBioscience	Cat# 17-5773-82, RRID:AB_469457
Goat Anti-Rabbit IgG Fc, Alexa Fluo 488	Thermo Fisher	Cat# A78953, RRID:AB_2925776
MYPT1 Polyclonal Antibody	Thermo Fisher	Cat# PA5-17164, RRID:AB_10978517

Chemicals, peptides, and recombinant proteins

Tert-Butyl Hydroperoxide Solution	Aladdin	Cat# B106035
Mouse Interleukin-2 (mIL-2)	Cell Signaling	Cat# NP_032392
HTH-01-015	TargetMol	Cat# 1613724-42-7
Filipin III	MCE	Cat# HY-N6718
Trolox	MCE	Cat# HY-101445
Simvastatin	MCE	Cat# HY-17502
Cholesterol	MCE	Cat# HY-N0322
H2DCFDA	MCE	Cat# HY-D0940
OVA peptide (257–264)	Sangon Biotech	Cat# T510212-0001
Geranylgeranyl Pyrophosphate	TargetMol	Cat# T36863
Alexa Fluor™ 594 Phalloidin	Thermo Fisher	Cat# A12381
Alexa Fluor™ 488 Phalloidin	Thermo Fisher	Cat# A12379
DAPI (4′,6-Diamidino-2-Phenylindole, Dihydrochloride)	Thermo Fisher	Cat# D1306
CellROX™ Deep Red Reagent	Thermo Fisher	Cat# C10422
D-Luciferin, Sodium Salt D	Yeasen	Cat# 40901ES08
Iron(II) perchlorate hydrate	Sigma-Aldrich	Cat# 334081
Polybrene	Sigma-Aldrich	Cat# A12379
Diphtheria Toxin	Sigma-Aldrich	Cat# D0564
Fluid Thioglycollate Medium	Hopebiol	Cat# HB5190-43
Fetal Bovine Serum	BDBIO	Cat# F814-500

Critical commercial assays

PrimeScript™ RT reagent Kit	Takara	Cat# RR037A
TB Green® Premix Ex Taq™ II	Takara	Cat# RR820A
ATP Assay Kit	Beyotime	Cat# S0026
Triton X-100	Beyotime	Cat# P0096
Nuclear and Cytoplasmic Protein Extraction Kit	Beyotime	Cat# P0028
Zombie NIR™ Fixable Viability Kit	Biolegend	Cat# 423105
Zombie Violet™ Fixable Viability Kit	Biolegend	Cat# 423113
Cell Activation Cocktail (with Brefeldin A)	Biolegend	Cat# 423304
QIAwave DNA Blood & Tissue Kit	QIAGEN	Cat# 69554
RNeasy Mini Kit	QIAGEN	Cat# 74104
Diaminobenzidine (DAB) Peroxidase Substrate Kit	Thermo Fisher	Cat# 34002
Annexin V Conjugates for Apoptosis Detection	Thermo Fisher	Cat# A13201
CellTrace™ CFSE Cell Proliferation Kit	Thermo Fisher	Cat# C34570
CellTrace™ Far Red Cell Proliferation Kit	Thermo Fisher	Cat# C34564
Amplex™ Red Cholesterol Assay Kit	Thermo Fisher	Cat# A12216
eBioscience™ Intracellular Fixation & Permeabilization Buffer Set	Thermo Fisher	Cat# 88-8824-00
Pierce™ BCA Protein Assay Kits	Thermo Fisher	Cat# 23225
60 kcal% fat high fat feed	ReadyDietech	Cat# d12492
Quick-Bands Fluorescent Loading Buffer	Sharebio	Cat# SB-ES008
Anti-Biotin MicroBeads	Miltenyi Biotec	Cat# 130-090-485
CD11c MicroBeads mouse	Miltenyi Biotec	Cat# 130-125-835

Oligonucleotides

hACAT2-F: GCGGACCATCATAGGTTCCTT	GENEWIZ	N/A
hACAT2-R: ACTGGCTTGTCTAACAGGATTCT	GENEWIZ	N/A
hFDFT1-F: CCACCCCGAAGAGTTCTACAA	GENEWIZ	N/A
hFDFT1-R: TGCGACTGGTCTGATTGAGATA	GENEWIZ	N/A
hGAPDH-F: AAGGTGAAGGTCGGAGTCAA	GENEWIZ	N/A
hGAPDH-R: AATGAAGGGGTCATTGATGG	GENEWIZ	N/A
hGCLC-F: GGAGGAAACCAAGCGCCAT	GENEWIZ	N/A
hGCLC-R: CTTGACGGCGTGGTAGATGT	GENEWIZ	N/A
hGCLM-F: TGTCTTGGAATGCACTGTATCTC	GENEWIZ	N/A
hGCLM-R: CCCAGTAAGGCTGTAAATGCTC	GENEWIZ	N/A
hHMGCR-F: TGATTGACCTTTCCAGAGCAAG	GENEWIZ	N/A
hHMGCR-R: CTAAAATTGCCATTCCACGAGC	GENEWIZ	N/A
hHMGCS1-F: GATGTGGGAATTGTTGCCCTT	GENEWIZ	N/A
hHMGCS1-R: ATTGTCTCTGTTCCAACTTCCAG	GENEWIZ	N/A
hNUAK1-F: GGGAGCTGTACGATTACATCAG	GENEWIZ	N/A
hNUAK1-R: ACACCGTTCTTGTGACAATAGTG	GENEWIZ	N/A
hSLC7A11-F: TCTCCAAAGGAGGTTACCTGC	GENEWIZ	N/A
hSLC7A11-R: AGACTCCCCTCAGTAAAGTGAC	GENEWIZ	N/A
hSQLE-F: GGCATTGCCACTTTCACCTAT	GENEWIZ	N/A
hSQLE-R: GGCCTGAGAGAATATCCGAGAAG	GENEWIZ	N/A
hSREBF2-F: CCTGGGAGACATCGACGAGAT	GENEWIZ	N/A
hSREBF2-R: TGAATGACCGTTGCACTGAAG	GENEWIZ	N/A
mAcat2-F: CCCGTGGTCATCGTCTCAG	GENEWIZ	N/A
mAcat2-R: GGACAGGGCACCATTGAAGG	GENEWIZ	N/A
mFdft1-F: ATGGAGTTCGTCAAGTGTCTAGG	GENEWIZ	N/A
mFdft1-R: CGTGCCGTATGTCCCCATC	GENEWIZ	N/A
mGapdh-F: AGGTCGGTGTGAACGGATTTG	GENEWIZ	N/A
mGapdh-R: TGTAGACCATGTAGTTGAGGTCA	GENEWIZ	N/A
mGclc-F: GGGGTGACGAGGTGGAGTA	GENEWIZ	N/A
mGclc-R: GTTGGGGTTTGTCCTCTCCC	GENEWIZ	N/A
mGclm-F: AGGAGCTTCGGGACTGTATCC	GENEWIZ	N/A
mGclm-R: GGGACATGGTGCATTCCAAAA	GENEWIZ	N/A
mGpx1-F: AGTCCACCGTGTATGCCTTCT	GENEWIZ	N/A
mGpx1-R: GAGACGCGACATTCTCAATGA	GENEWIZ	N/A
mGpx8-F: CCTTTCGCTGCCTACCCATTA	GENEWIZ	N/A
mGpx8-R: GAGTAGAAGCTGTTGGTTCTCG	GENEWIZ	N/A
mHmgcr-F: TGTTCACCGGCAACAACAAGA	GENEWIZ	N/A
mHmgcr-R: CCGCGTTATCGTCAGGATGA	GENEWIZ	N/A
mHmgcs1-F: AACTGGTGCAGAAATCTCTAGC	GENEWIZ	N/A
mHmgcs1-R: GGTTGAATAGCTCAGAACTAGCC	GENEWIZ	N/A
mNuak1-F: GATGGCCCTGCTGTAGAGAC	GENEWIZ	N/A
mNuak1-R: TGTTTGTGGTGATGTCGCTTC	GENEWIZ	N/A
mSod1-F: AACCAGTTGTGTTGTCAGGAC	GENEWIZ	N/A
mSod1-R: CCACCATGTTTCTTAGAGTGAGG	GENEWIZ	N/A
mSqle-F: AGTTCGCTGCCTTCTCGGATA	GENEWIZ	N/A
mSqle-R: GCTCCTGTTAATGTCGTTTCTGA	GENEWIZ	N/A
mSrebf1-F: GCAGCCACCATCTAGCCTG	GENEWIZ	N/A
mSrebf1-R: CAGCAGTGAGTCTGCCTTGAT	GENEWIZ	N/A
mXbp1-s-F: GCTGAGTCCGCAGCAGGT	GENEWIZ	N/A
mXbp1-s-R: CAGGGTCCAACTTGTCCAGAAT	GENEWIZ	N/A
mXbp1-u-F: CAGACTACGTGCACCTCTGC	GENEWIZ	N/A
mXbp1-u-R: CAGGGTCCAACTTGTCCAGAAT	GENEWIZ	N/A
sgFdft1#1: 5′-CAGTACTGCCACTACGTTGC-3′	GENEWIZ	N/A
sgFdft1#2: 5′-CCACCAGCCCAGCAACGTAG-3′	GENEWIZ	N/A
sgFdft1#3: 5′-AGTGCTGGAGGACTTCCCCA-3′	GENEWIZ	N/A
sgGgps1#1: 5′-AAAGTATTGTGTGCAGTACC-3′	GENEWIZ	N/A
sgGgps1#2: 5′-GTGAAAAGCTTCACCGCATC-3′	GENEWIZ	N/A
sgGgps1#3: 5′-GGTGAGAAGCAAACTTTCAC-3′	GENEWIZ	N/A
sgHmgcr#1: 5′-GGATGCATGGCCTCTTCG-3′	GENEWIZ	N/A
sgHmgcr#2: 5′-GTTCCCACAATAACTTCCCA-3′	GENEWIZ	N/A
sgHmgcr#3: 5′-GTTGCCGGTGAACATGTTCA-3′	GENEWIZ	N/A
sgNuak1#1: 5′-GGTCCACCGGGACTTGAAGC-3′	GENEWIZ	N/A
sgNuak1#2: 5′-GTCCTTCTGGTACAGGT-3′	GENEWIZ	N/A
sgNuak1#3: 5′-GCTTTACACTCTCATTTA-3′	GENEWIZ	N/A
sgNUAK1#1: 5′-GTGATCGAAACCATCGAA-3′	GENEWIZ	N/A
sgNUAK1#2: 5′-GTACAGCTCCCCTTTGC-3′	GENEWIZ	N/A
sgNUAK1#3: 5′-GAGGACATTGCCAACCAC-3′	GENEWIZ	N/A
sgSqle1#1: 5′-AGTGTCGACCTCGTTCGTGA-3′	GENEWIZ	N/A
sgSqle1#2: 5′-CACGAACGAGGTCGACACTG-3′	GENEWIZ	N/A
sgSqle1#3: 5′-TGTAATCGGCGTGCAATACA-3′	GENEWIZ	N/A
sgXbp1#1: 5′-GCTCCAGCTCGCTCATCC-3′	GENEWIZ	N/A
sgXbp1#2: 5′-GGAGTTAAGAACACGCTT-3′	GENEWIZ	N/A
sgXbp1#3: 5′-GGAGAAAACTCACGGCCTTG-3′	GENEWIZ	N/A

Recombinant DNA

pMD2.G	Addgene	Cat# 12259
psPAX2	Addgene	Cat# 12260
Mouse Kinome CRISPR Knockout Library (Brie)	Addgene	Cat# 75316
lentiGuide-puro	Addgene	Cat# 52963
pLEX_307	Addgene	Cat# 41392
lentiCas9-Blast	Addgene	Cat# 52962
pLenti-CMV-Puro-DEST	Addgene	Cat# 14752
pLenti-CMV-Blast-DEST	Addgene	Cat# 17451

Experimental models: Organisms/strains

Mouse: C57BL/6	Shanghai Model Organisms Center	Cat# SM-001
Mouse: BALB/c	Shanghai Model Organisms Center	Cat# SM-003
Mouse: Tcra-KO (OT-1) mice	Shanghai Model Organisms Center	Cat# NM-KO-190434
Mouse: Itgax-2A-tdTomato-2A-DTR mice	Shanghai Model Organisms Center	Cat# NM-KI-204992
Mouse: BALB/c Nude mice	Shanghai Model Organisms Center	Cat# SM-014

Experimental models: Cell lines

Cell line: MC38	Cobioer Biosciences	Cat# CBP60825
Cell line: AKR	Qingqi Biotechnology	Cat# BFN60805936
Cell line: B16-F10	Pricella Biotechnology	Cat# CL-0319
Cell line: 293T	Pricella Biotechnology	Cat# CL-0005
Cell line: CT26	Pricella Biotechnology	Cat# CL-0071
Cell line: SW480	Pricella Biotechnology	Cat# CL-0223B
Cell line: THP-1	Pricella Biotechnology	Cat# CL-0233
Cell line: HCT116	Pricella Biotechnology	Cat# CL-0096
Cell line: TE-1	Pricella Biotechnology	Cat# CL-0231
Cell line: MC38-Cas9	This paper	N/A
Cell line: MC38-luc2	This paper	N/A
Cell line: MC38-OVA	This paper	N/A

Deposited data

Bulk RNA-seq raw data	This paper	GEO: GSE264513
Single-cell RNA-seq raw data	This paper	GEO: GSE283933

Software and algorithms

Graphad Prism 10	Graphpad	https://www.graphpad.com
BioRender	BioRender	https://BioRender.com/l98x498
ImageJ	National Institutes of Health	https://imagej.net/ij/download.html
FlowJo (v10.8.0)	FlowJo	https://www.flowjo.com
MAGeCK	Li et al.⁶⁹	https://bitbucket.org/liulab/mageck/src/master
GSEA	Subramanian et al.	https://www.gsea-msigdb.org/gsea/index.jsp
Cell Ranger (v 5.0.1)	10x Genomics	https://www.10xgenomics.com/support/software/cell-ranger/latest
Seurat v5	Stuart et al.⁷⁰	https://satijalab.org/seurat
R (v4.3.2)	R (v4.3.2)	https://www.r-project.org
R code	This paper	https://github.com/MrmissG/nuak1-scRNA-seq

Open in a new tab

Experimental model and study participant details

Cell lines and plasmids

MC38 cell line was purchased from Nanjing Cobioer Biosciences (Nanjing, China). AKR cell line was from the Qingqi Biotechnology (Shanghai, China). B16-F10, 293T, CT26, SW480, HCT116, TE-1, and THP-1 cell lines were purchased from Pricella Biotechnology (Wuhan, China). Cell lines were authenticated and validated to be mycoplasma-free. HCT116, CT26, TE-1, and THP-1 cells were cultured in RPMI-1640 (Gibco), and MC38, AKR, B16-F10, SW480, 293T cells were cultured in DMEM (Gibco), supplemented with 1% penicillin/streptomycin and 10% FBS (BDBIO, Hangzhou, China). All cells were cultured at 37°C with 5% CO₂. Knockout and overexpression cell lines were constructed by lentiviral infection, as previously described.⁷¹ Lentiviral vectors used for Cas9 expression and sgRNA were lentiCas9-Blast (Addgene #52962) and lentiGuide-Puro (Addgene #52963). The sgRNA sequences are shown in the key resources table. A combination of three sgRNA sequences was used together to generate knockouts in each target gene. The lentiviral vector used for overexpressing Nuak1 was pLEX_307 (Addgene #41392). Lentiviral vectors used for OVA and luc2 expression were pLenti-CMV-Puro-DEST (Addgene #14752) and pLenti-CMV-Blast-DEST (Addgene #17451). Lentiviruses were packaged in 293T cells using pMD2.G (Addgene #12259) and psPAX2 (Addgene #12260). Target cells were transduced with lentiviruses with 6 μg/mL polybrene (Sigma-Aldrich).

Mice

C57BL/6, BALB/c, OT-1, BALB/c Nude and Itgax-2A-tdTomato-2A-DTR (CD11c-DTR) mice were purchased from the Shanghai Model Organisms Center. All mice used in our experiments were six to eight weeks old, and housed under specific pathogen-free conditions. Female animals were involved, and sex was not considered as a biological variable. All animal procedures were approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University (Shanghai, China).

Method details

CRISPR-Cas9 screening in MC38 cells

Lentiviral Mouse Kinome CRISPR Knockout Library (Brie) (Addgene #75316) was transduced into Cas9-expressing MC38 cells, as previously described.⁷² Briefly, ∼30 million MC38 cells stably expressing Cas9 were infected with the CRISPR knockout library at a multiplicity of infection (MOI) of 0.3. Cells expressing sgRNAs were selected using puromycin (2 μg/mL) for 5 days. Cells displaying ecto-CALR and negative for propidium iodide (PI-) were isolated via flow cytometry. Genomic DNA of isolated cells was extracted using QIAwave DNA Blood & Tissue Kit (QIAGEN). The sgRNA sequences were amplified and prepared for sequencing, and NGS was performed on Illumina HiSeq to determine sgRNA abundance. After trimming adaptor sequences using Cutadapt, sgRNAs were mapped and normalized, and the significantly enriched or depleted sgRNAs from any comparison of conditions were identified by the MAGeCK algorithm.⁶⁹

Quantitative PCR (qPCR)

Total RNA was extracted from the cells using RNeasy Mini Kit (Qiagen). The cDNA synthesis was performed using PrimeScript RT Reagent Kit (Takara). qPCR experiments were conducted using TB Green Premix Ex Taq II (Takara) and ABI 7900HT Fast Real-Time PCR System (Applied Biosystems). mRNA expressions of target genes were normalized to GAPDH and calculated by the ΔΔC_t method.

Western blotting analysis

Total proteins of cancer cells were collected with RIPA lysis buffer (Thermo Fisher) supplemented with protease and phosphatase inhibitors (Roche). Nuclear and cytoplasmic extracts from tumor cells were prepared with a Nuclear Protein Extraction Kit (Beyotime). Proteins from cell culture supernatants were harvested by Amicon Ultra 15 mL Centrifugal Filters and quantified by Quick-Bands fluorescent loading buffer (Sharebio). Protein concentrations were determined by a Pierce bicinchoninic acid (BCA) protein assay kit (Thermo Fisher). 20–30 mg of proteins was subjected to SDS-PAGE and immunoblotting. To detect the concentration of the supernatant protein, the sample was mixed with the Quick-Bands fluorescent loading buffer (Sharebio) and then subjected to SDS-PAGE. The protein bands were visualized by UV imaging, and the quantification was performed using ImageJ. Primary antibodies used for western blotting were purchased from Cell Signaling and Abcam, listed in the key resources table.

ATP quantification

The ATP assay kit was from Beyotime and the assay was performed according to the manufacturer’s instructions. Cell culture mediums were centrifuged. Supernatants were mixed with the ATP detection working solution in a white 96-well plate. RLU was measured with a microplate reader (Snergy2).

Cholesterol content measurement

For measurement of cellular cholesterol content, cells were stained with Filipin III (MCE), washed three times with PBS and analyzed by flow cytometry. For additional verification and specific quantification of cholesterol content, cholesterol was also measured using the Amplex Red Cholesterol Assay Kit (Invitrogen) according to the manufacturer’s instructions.

Cholesterol supplementation experiments

To investigate the impact of exogenous cholesterol supplementation on HTH-induced immunogenic cell death, tumor cells were subjected to treatment with HTH-01-015 at a concentration of 20 μM, or in combination with cholesterol at 10 and 50 μM. The treatment duration was 24 h for assessing the expression of ecto-CALR, and 6 h for the measurement of ROS.

Transmission electron microscope (TEM) imaging

Cells were plated in 10 cm cell dish and then pretreated with corresponding factors for 24 h. Next, cells were collected and fixed with 2.5% glutaraldehyde. TEM imaging was conducted by Servicebio, Wuhan, China. The quantifications of ER and cytoplasmic areas were performed using ImageJ.

Tumor phagocytosis assay

Murine peritoneal macrophages were obtained by injecting 1–2 mL of 3% thioglycollate medium (Hopebiol) into the peritoneum 5 days prior to cell harvest. Peritoneal marophages were isolated and cultured as described before.⁷³^,⁷⁴ BMDCs were isolated and differentiated from mouse bone marrow cells with 20 ng/mL GM-CSF and 10 ng/mL IL-4 for one week. MC38 tumor cell lines were labeled with CFSE (Thermo Fisher) and treated with vehicle, 10 μM or 20 μM HTH-01-015 for 24h, then cocultured with Far-red-labeled (Thermo Fisher) BMDCs or macrophages in a 1:1 ratio for 12h. The phagocytosis of BMDCs and macrophages was evaluated by FACS.

Animal tumor models

MC38, AKR, (wild-type, luc2-expressing, OVA-expressing, various CRISPR-KO lines), and B16-F10 (1.0 × 10⁶/mouse) cells were injected subcutaneously at the right lower flank of C57BL/6, BALB/c Nude, and Itgax-2A-tdTomato-2A-DTR mice, CT26 (Nuak1-overexpressing and control) were injected at BALB/c mice, using the same quantity and method. Tumors were measured every 2–3 days using a digital caliper, and the tumor volume was calculated using the following formula: V = L × W²/2, where L and W are the long and short diameters of the tumor. Mice in the high cholesterol diet (HCD) group were fed a high-fat diet (ReadyDietech) starting one week before tumor injection. For CD11c⁺ cell population ablation, Itgax-2A-tdTomato-2A-DTR mice were injected intraperitoneally with 15 ng/g diphtheria toxin (Sigma-Aldrich) every three days.

For imaging of MC38-luc2 tumors in live animals, D-Luciferin Potassium Salt (Yeasen) was dissolved in sterile distilled water (final concentration: 15 mg/mL). Mice were anesthetized with isoflurane and injected intraperitoneally with 100 μL of the luciferin solution. After 10 min, images were acquired with the IVIS Lumina series III (PerkinElmer).

Inhibitors treatments for animals

Tumors were treated when tumor size reached ∼100 mm³, HTH-01-015 was injected intratumorally at a dose of 15 mg/kg every other day. Simvastatin was injected intraperitoneally at a dose of 50 mg/kg simultaneously with HTH-01-015 every other day.

Antibodies treatments for animals

For antibody treatment, mice were administered intraperitoneally with 200 μg/mouse anti-CD8a (clone 2.43, BioXCell) to deplete CD8⁺ T cells, beginning two days before tumor implantation, followed by similar dosing with 100 μg/mouse every 4 days throughout tumor growth. Anti-4-1BB (clone LOB12.3, BioXCell) and anti-GITR (clone DTA-1, BioXCell) were administered by intraperitoneal injections every 3–4 days, at a dose of 100 μg for anti-4-1BB and anti-GITR per injection. Anti-PD1 (clone RMP1-14, BioXCell) was administered by intraperitoneal injections every 7 days, at a dose of 100 μg.

OT-1 T cell adoptive transfer

Spleens and peripheral lymph nodes were harvested from OT-1 mice purchased from the Shanghai Model Organisms Center and dissociated to obtain a single-cell suspension. Red blood cells were lysed with RBC lysis buffer (BioLegend). Cells were resuspended at 1×10⁶ cells/mL in RPMI +5% FBS +1% penicillin/streptomycin. Medium was supplemented with OVA peptide (257–264) at 5 μg/mL and mouse interleukin-2 (mIL-2) at 50 U/mL. Cells were used for adoptive transfer following 4 days of activation. 1×10⁶ cells activated T cells were injected intravenously in 100 μL of PBS every 3 days.

T cell co-culture assay

MC38 tumor cells with OVA-antigen expression were treated with vehicle or 20 μM HTH-01-015 for 24h. Then changed the cell culture medium and cocultured tumor cells with bone marrow-derived dendritic cells (BMDCs) in equal ratio overnight. Subsequently, isolated the BMDCs with anti-CD11c microbeads (Miltenyi Biotec) and cocultured with OT-1 cells in equal ratio for 12h. The activation markers CD69 and CD107a of OT-1 cells were evaluated with FACS.

Flow cytometry

Primary antibodies used for flow cytometry were purchased from BioLegend, BD Biosciences, and Abcam, as shown in the key resources table. For in vitro flow cytometry, the adherent cells were detached using 10 mM EDTA, then washed with PBS and stained with antibodies for FACS analysis.

To analyze tumor-infiltrating immune cells, subcutaneously implanted tumors were dissected and transferred into RPMI 1640 medium, disrupted mechanically with scissors, digested using a mouse tumor dissociation kit and a gentleMACS Octo Dissociator (Miltenyi Biotec) at 37°C, and dispersed through a 40-μm cell strainer (BD Biosciences). Single cells were further washed and stained with antibodies. Dead cells were excluded by staining with a Zombie fixable viability kit (BioLegend). To detect intracellular cytokine expression, separated cells were stimulated for 6h with cell activation cocktail (BioLegend). Fluorescence data were acquired on a BD LSRFortessa cell analyzer (BD Biosciences) and analyzed using FlowJo software. Gating strategies were shown in Figure S8.

Immunohistochemistry and immunofluorescence

Expression of HMGB1, 8-Oxo-dG were assessed by immunohistochemistry using anti-HMGB1 (Abclonal), anti-8-Oxo-dG (Abcam) and detected using a diaminobenzidine (DAB) peroxidase substrate kit (Thermo).

CALR, HMGB1, NRF2, and cholesterol were detected by immunofluorescence using anti-CALR (Abcam), anti-HMGB1 (Abclonal), anti-NRF2 (Cell Signaling) and Filipin III (MCE). The nucleus was stained with DAPI and visualized under Leica DM6B fluorescence microscope or Leica TCS SP5 Confocal. Cells were cultured on glass coverslips and treated with HTH-01-015 or left untreated. Cells were washed, fixed in 1% paraformaldehyde (PFA) in PBS for 15 min at RT. Cells were permeabilized with 0.5% Triton X-100 (Beyotime) for 5 min. Cells were blocked and subsequently incubated with 5% bovine serum albumin (BSA) for 30 min at RT. After extensive wash, cells were stained with primary antibody in 5% BSA for 45 min at RT, followed by incubation with secondary Alexa conjugates, for 30 min at RT. Cells were then fixed in 4% PFA/PBS for 20 min at RT. Nuclei were stained with DAPI for 5 min at RT.

RNA sequencing and public datasets analysis

Transcriptome sequencing and data analysis were performed by Novogene. Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set enrichment analysis (GSEA) of differentially expressed genes was implemented by the cluster Profiler R package.

Patients’ survival information and expression of NUAK1 in human immune cells were collected from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). GEPIA database (https://gepia.cancer-pku.cn/) was used for analyzing the correlation of genes expression level with the prognosis of cancer patients. Co-expression of genes in patient tumors was analyzed using the cBioPortal (https://www.cbioportal.org).

Sterolomics analysis

Sterolomics analysis was conducted at LipidALL Technologies as previously described.⁷⁵ Lipids were extracted from cells using a modified version of the Bligh and Dyer’s protocol. Oxysterols and sterols were derivatized to obtain their picolinic acid esters prior to LC/MS analysis on a Thermo Fisher U3000 DGLC coupled to Sciex QTRAP 6500 Plus, and quantitated by referencing the spiked internal standards.

In vitro ROS assay

Cholesterol was added in varying concentrations (100 μM, 300 μM) to H₂O₂ (300 μM, 500 μM) or Fe(ClO₄)₂-xH₂O (100 μM) plus H₂O₂ (1 mM). ROS was detected with H₂DCFDA every 5 min according to the manufacturer’s instruction. Fluorescence was measured with a microplate reader at excitation/emission wavelength of 500/520 nm.

Single-cell RNA sequencing (scRNA-seq)

Mouse tumors (each set containing three independent samples) were washed with ice-cold PBS and cut into small pieces, which were then subjected to enzymatic digestion with collagenase D and DNase I (Roche) using a gentleMACS Tissue Dissociator (Miltenyi Biotec). Cells were filtered through a 70-μm filter and washed with PBS. To extract immune cells, cells were stained with biotin anti-mouse CD45 antibody (Biolegend) and separated by anti-biotin MicroBeads (Miltenyi Biotec) according to the manufacturer’s protocol. Sorted immune cells with a purity greater than 95% and viability higher than 85% were subjected to 10x Genomics scRNA-seq. To control the final number of cells captured, approximately 30,000 cells per sample were loaded onto 10X Chromium Single Cell Platform (10X Genomics) at a concentration of 1,000 cells per μL (Single Cell 3′ library and Gel Bead Kit v.3) as described in the manufacturer’s protocol. Finally, the library pool was prepared, and sequencing was performed on an Illumina NovaSeq 6000 platform.

The CellRanger (v 5.0.1) was used to align the clean reads with mm10. The Seurat⁷⁰ (v 5.1.0) pipeline was integrated into analysis and visualization, including clustering, dimension reduction, and cell type identification. Cell types were annotated based on the expression of known markers shown in Figure S3E. The genes expressed in less than three cells were removed. The cells that expressed less than 200 genes were not included in the analysis. The harmony was used to remove the batch effects between samples. The dimension reduction of umap was calculated based on 40 harmony components. The significantly up-regulated genes in the specific sample and/or subpopulation were identified by FindMarkers and FindAllMarkers. The clusterProfiler was used to annotate the top markers of each subpopulation with the KEGG database. The macrophage gene score was evaluated with AddModuleScore. The ggplot2 was used to display the expression patterns of gene modules.

Quantification and statistical analysis

All described results are representative of at least three independent experiments. The number of samples (n) was described in detail for each figure panel. Statistical analyses were performed using Prism 10 (GraphPad). Unpaired Student’s t test was used for the comparison between two groups. One-way analysis of variance (ANOVA) or two-way ANOVA followed by the Sidak’s test was used for the multiple comparisons. Results are presented as mean ± SEM unless otherwise indicated. p < 0.05 was considered statistically significant.

Published: January 16, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2024.101913.

Supplemental information

Document S1. Figures S1–S8

mmc1.pdf^{(9.4MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(16.5MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S8

mmc1.pdf^{(9.4MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(16.5MB, pdf)}

Data Availability Statement

•
The raw sequencing data have been deposited in the Gene Expression Omnibus database and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
•
Original code has been deposited at GitHub and is publicly available as of the date of publication. The repository in GitHub is listed in the key resources table.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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PERMALINK

Targeting the mevalonate pathway potentiates NUAK1 inhibition-induced immunogenic cell death and antitumor immunity

Liming Gui

Kaiwen Chen

Jingjing Yan

Ping Chen

Wei-Qiang Gao

Bin Ma

Summary

Graphical abstract

Highlights

Introduction

Results

NUAK1 inhibition elicits immunogenic death of tumor cells

Figure 1.

NUAK1 inhibition triggers ICD via ROS-induced ER stress

Figure 2.

NUAK1 blockade suppresses tumor growth by promoting antitumor immunity

Figure 3.

NUAK1 blockade leads to hyperactivation of the mevalonate pathway and cholesterol biosynthesis

Figure 4.

Targeting the mevalonate pathway sensitizes tumor cells to NUAK1 inhibition-induced ICD

Figure 5.

Cholesterol impairs NUAK1 inhibition-induced ICD and antitumor effect

Figure 6.

Dual inhibition of NUAK1 and the mevalonate pathway enhances immune checkpoint blockade efficacy

Figure 7.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Cell lines and plasmids

Mice

Method details

CRISPR-Cas9 screening in MC38 cells

Quantitative PCR (qPCR)

Western blotting analysis

ATP quantification

Cholesterol content measurement

Cholesterol supplementation experiments

Transmission electron microscope (TEM) imaging

Tumor phagocytosis assay

Animal tumor models

Inhibitors treatments for animals

Antibodies treatments for animals

OT-1 T cell adoptive transfer

T cell co-culture assay

Flow cytometry

Immunohistochemistry and immunofluorescence

RNA sequencing and public datasets analysis

Sterolomics analysis

In vitro ROS assay

Single-cell RNA sequencing (scRNA-seq)

Quantification and statistical analysis

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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