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Checkpoint kinase 1/2 inhibition potentiates anti-tumoral immune response and sensitizes gliomas to

Issuing time:2023-03-24 11:52

Abstract

Whereas the contribution of tumor microenvironment to the profound immune suppression of glioblastoma (GBM) is clear, tumor-cell intrinsic mechanisms that regulate resistance to CD8 T cell mediated killing are less understood. Kinases are potentially druggable targets that drive tumor progression and might influence immune response. Here, we perform an in vivo CRISPR screen to identify glioma intrinsic kinases that contribute to evasion of tumor cells from CD8 T cell recognition. The screen reveals checkpoint kinase 2 (Chek2) to be the most important kinase contributing to escape from CD8 T-cell recognition. Genetic depletion or pharmacological inhibition of Chek2 with blood-brain-barrier permeable drugs that are currently being evaluated in clinical trials, in combination with PD-1 or PD-L1 blockade, lead to survival benefit in multiple preclinical glioma models. Mechanistically, loss of Chek2 enhances antigen presentation, STING pathway activation and PD-L1 expression in mouse gliomas. Analysis of human GBMs demonstrates that Chek2 expression is inversely associated with antigen presentation and T-cell activation. Collectively, these results support Chek2 as a promising target for enhancement of response to immune checkpoint blockade therapy in GBM.

Introduction

Gliomas and glioblastoma (GBM) in particular, are the most common malignant primary brain tumors in adults1. Unfortunately, despite extensive research and the use of multimodal therapeutic strategies, the median overall survival time is still relatively short1. These tumors have a complex relationship with the immune system and rely on multiple mechanisms of immune suppression to inhibit anti-tumor immune responses including the induction of T-cell anergy, exhaustion, apoptosis, and sequestration in the bone marrow2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18. Immune checkpoint inhibitors have shown remarkable responses in some malignancies19, especially those that are heavily infiltrated with T cells and have a high tumor-mutational burden. However, despite the use of a wide variety of immune therapeutic strategies such as vaccines, dendritic cells, adjuvants, adoptive cellular therapies, and immune checkpoint inhibitors, immunotherapy has not shown efficacy for gliomas20.

In recent analyses of recurrent GBM patients that were treated with PD-1 immune checkpoint blockade, we showed that mitogen-activated protein kinase (MAPK) signaling activation in tumor cells was associated with response to this therapy21,22 highlighting the role of tumor kinases in immune modulation. Kinases are often altered in cancer-driving intracellular signaling cascades23. Moreover, kinases are targetable with small molecule inhibitors, and some kinase inhibitors are blood-brain barrier (BBB) permeable. Thus targeting kinases is a potentially relevant translational approach to the modulation of anti-tumoral immunity for GBM24. A previous in vitro CRISPR screen identified the SWI/SNF chromatin remodeling complex in tumor cells as an epigenetic modulator of T-cell recognition through the IFN-γ pathway25. Using an in vivo CRISPR screen approach to investigate the causal relationship between kinases and anti-tumoral immunity is relevant since it can capture the influence of the tumor microenvironment.

In this work, to identify tumor intrinsic kinase that contributes to the evasion of CD8 T cells in an unbiased fashion, we perform an in vivo kinome knockout (KO) CRISPR screen evaluating 713 kinases in glioma cells intracranially implanted into wild-type (WT) and CD8 KO mice. KO of checkpoint kinase 2 (Chek2), a kinase whose canonical function relates to DNA-damage response, shows the highest depletion among all the kinases, in tumors implanted into immune-competent mice relative to CD8 KO hosts. In other words, out of all the kinases, knockout of Chek2 in glioma tumor cells sensitizes them to CD8 T-cell-mediated killing. In this study, we characterize the effect of Chek2 inhibition/depletion on the response to PD-1 or PD-L1 blockade in the murine glioma models. The immune responsiveness upon Chek2 inhibition/depletion shows association with enhanced antigen presentation and increased type I interferon response manifested by low Chek2 expressing tumor cells in preclinical models and in human GBM scRNA-seq datasets.

Results

In vivo kinome knockout CRISPR screen identifies kinases implicated in CD8 T-cell immune evasion

CD8 T cells are the predominant effector population in cancer mediated immunity26. To interrogate the interaction between tumor intrinsic kinases and CD8 T cells in gliomas, we first performed immune profiling to characterize immune cell types in WT and CD8 deficient (CD8 KO) mice. Our flow cytometry data confirmed the absence of CD8 T cells in CD8 KO mice, whereas all other immune cell types such as CD4 T cells, NK cells and macrophages remained unchanged between the WT and CD8 KO mice (Supplementary Fig. 1a, b). Furthermore, there were no survival differences between WT and CD8 KO C57BL/6 mice implanted with GL261 glioma cells (n = 9/group, p = 0.2) (Fig. 1a) which remained consistent with the prior studies27. To investigate the contribution of glioma cell-intrinsic kinases in T-cell recognition, GL261 cells were intracranially implanted after they had been transfected with a KO CRISPR library for all 713 known kinases so that a single kinase was deleted per cell (Fig. 1b). To identify the relative contribution of each of these kinases in evading CD8 T-cell-mediated killing, the kinome KO glioma cells were implanted in both WT and CD8 KO C57BL/6 mice (Fig. 1b). As mice approached the endpoint, they were euthanized and the tumor region was excised, genomic DNA was extracted, and the guides were amplified and sequenced (Supplementary Fig. 2a, b). A difference in survival, trending towards significance, was seen between WT (n = 11) relative to CD8 KO mice (n = 9; p = 0.06), implanted with kinome KO GL261 glioma cells (Fig. 1c), suggestive of CD8 T-cell-mediated selection in WT mice implanted with kinome KO GL261 glioma cells. The normalized kinase gRNA read counts were used to calculate fold change depleted and fold change enriched in WT relative to the CD8 KO mice group (Fig. 1d). Kinase KO clones that were enriched in WT mice as compared to the CD8 KO mice were the kinases that contributed towards susceptibility to CD8 T-cell cytotoxicity (Fig. 1e) while kinase KO clones depleted in WT mice relative to the CD8 KO mice contributed towards resistance to CD8 T-cell-mediated killing (Fig. 1f). Glioma clones with Chek2 KO were the most depleted sgRNAs in mice with intact immunity as compared to the CD8 KO mice (p < 0.0001).

a KM survival curves of the C57BL/6 WT and CD8 KO mice (n = 9/group) bearing GL261 glioma. The median survival durations in the groups were: WT, 21 days; CD8 KO, 20 days; Statistics: WT versus CD8 KO, log-rank test p = 0.2. b Schematic representation of the in vivo CRISPR screen. Mouse glioma cells GL261 were transduced with kinome knockout library and the transformed cells were implanted in WT and CD8 KO mice. The library representation of >500X was maintained in the WT and CD8 KO group by injecting 2 × 105 cells/mouse. As the animals approached the endpoint, they were sacrificed, and the genomic DNA was extracted from the tumor region, guides were amplified, and sequenced. c KM analysis of the animals in the CRISPR screen 1. The KM plot shows percent survival of WT (n = 11) and CD8 KO (n = 9) animals bearing kinome KO GL261 glioma cells. The median survival durations in the groups were as follows: WT, 21 days; CD8 KO, 18 days; Statistics: WT versus CD8 KO, log-rank test p = 0.06. d Scatter plot showing the top kinases with the most enriched or depleted sgRNAs in the WT as compared to the CD8 KO animals. The sky blue dots correspond to the top depleted sgRNAs, while the red dots represent the most enriched sgRNAs in the WT as compared to the CD8 KO animals. The gray dots are all other sgRNAs. e The Y-axis shows fold change (fc) WT over CD8 KO of the normalized sgRNA counts. The red dots correspond to the top enriched sgRNAs and the fluorescent dots are all non-targeting sgRNAs. f The Y-axis shows fc WT over CD8 KO of the normalized sgRNA counts. The blue dots correspond to the top depleted sgRNAs and the fluorescent dots are all non-targeting sgRNAs. For e, f the error bars on non-targeting sgRNAs represent mean ± SD. Source data for a, c–f are provided as a Source Data file.

To further validate our initial CRISPR screen, we performed a second, independent CRISPR screen using GL261 glioma model where we quantified the Chek2 KO clones in intracranial gliomas in WT and CD8 KO mice hosts over time. We found negative selection of Chek2 KO clones in mice with WT CD8. As shown in Fig. 2a, b, we collected samples at early stage and late stage of the screen. Similar to the first CRISPR screen, the second CRISR screen demonstrated that the selection of Chek2 KO glioma cells over time is specific to WT mice and is absent in CD8 KO mice (Fig. 2c–e). In the first CRISPR screen, 3/4 Atm (an upstream activator of Chek2) sgRNAs had normalized counts <5, which prevented further analysis (Fig. 2f). However, CRISPR screen 2 showed similar depletion of Atm sgRNAs over time as Chek2 (Fig. 2g, h) in WT mice group as compared to the CD8 KO group. However, unlike Chek2, selection of its functionally related kinase Chek1, was not specific to WT mice (Fig. 2i–k). In other words, Chek1 KO clones were significantly depleted in both WT and CD8 KO mice compared to the non-targeting sgRNAs (Fig. 2l, m). The second CRISPR screen validated our initial CRISPR screen findings.

a Schematic representation of the second in vivo CRISPR screen. The library representation of 733X was maintained in the WT (n = 11) and 800X in the CD8 KO (n = 12) group by injecting 200,000 cells/mouse. Different barcodes were assigned to the animals that were sacrificed at the early stage (D18-D23) and animals sacrificed at the late stage (D24–D38) from both WT and CD8 KO hosts, guides were amplified and sequenced. b KM analysis of the animals in the second CRISPR screen. The KM plot shows percent survival of wild-type and CD8 KO animals bearing kinome KO glioma cells. p = 0.02 using the log-rank test. c The histogram shows 74-fold enrichment of Chek2 sgRNAs in CD8 KO mice as compared to the WT mice in the CRISPR screen 1. In the CRISPR screen 2, d, e Change in Chek2 KO glioma cells over time in WT and in CD8 KO mice respectively. f The histogram shows change in Atm sgRNAs in CD8 KO mice as compared to the WT mice in the CRISPR screen 1. In the CRISPR screen 2, g, h Change in Atm KO glioma cells over time in WT and in CD8 KO mice respectively. i The histogram shows fold change in Chek1 sgRNAs in CD8 KO mice as compared to the WT mice in the CRISPR screen 1. In the CRISPR screen 2, j, k Depletion of Chek1 KO glioma cells over time in WT and in CD8 KO mice respectively. The distribution of non-targeting sgRNAs over time in WT mice (l) and CD8 KO mice (m) of the CRISPR screen 2. For c–m the error bars represents mean ± SD. For d–m statistics was done using unpaired two-tailed t-test (without adjustments for multiple comparisons). Source data for Fig. b–m are provided as a Source Data file.

Tumor cell intrinsic CHEK2 inversely correlates with type I interferon response in human GBMs and dampens this response in mouse glioma cells

To understand the phenotype of T cells and tumor cells associated with low and high CHEK2 expression in tumor cells, we interrogated the single-cell RNA sequencing (scRNA-seq) data of 28 GBM patients28. We analyzed 7930 cells and stratified tumor cells as expressing high or low levels of CHEK2 using the median as cutoff value. In this dataset, cells were categorized as tumor cells, macrophages, oligodendrocytes, or T cells based on the expression of specific gene sets (as described by Neftel et al.28) (Fig. 3a). CHEK2 appeared to be primarily expressed in macrophages and tumor cells of the human GBM specimens (Fig. 3b, c). Next, we analyzed the gene expression pattern of T cells associated with low and high CHEK2 expressing tumor cells. T cells from the low CHEK2 expressing tumor cells showed gene ontology (GO) term enrichment for “Interferon γ (IFN-γ) signaling” as compared to the T cells from high CHEK2 expressing tumor cells (p = 0.0005; Fig. 3d) (Supplementary Data 1). On the tumor-cell compartment, the low CHEK2 expressing tumor cells showed GO term enrichment for “Interferon type I response” as compared to the tumor cells from high CHEK2 expressing tumor cells (p = 0.032; Fig. 3e). Further, we analyzed another independent scRNA-seq dataset to assess the phenotype of the tumor cell in the context of CHEK2 expression29. This scRNA-seq data also showed enrichment of “Interferon type I response pathway” (p = 2.8e−13) in low CHEK2 expressing tumor cells as compared to the high CHEK2 expressing tumor cells (Supplementary Fig. 3a, b). To investigate whether correlations seen in human GBM patients have a causal relationship, we generated a Chek2 KO clone in the GL261 mouse glioma cell line using the CRISPR cas9 system. A scrambled guide control was generated and designated as a non-targeting control (NTC). The KO was confirmed using western blotting (Fig. 3f). Next we treated Chek2 KO GL261 clones with IFN-γ to test its responsiveness to IFN-γ signaling. We found significantly increased surface expression of PD-L1, a known IFN-γ inducible gene30, on the Chek2 KO relative to the control cells upon stimulation with IFN-γ (p = 0.003; Fig. 3g) (Supplementary Fig. 4). Along the same lines, Chek2 KO clones demonstrated increased baseline mRNA expression levels of type I interferon-response genes IFN-β, IRF7, ISG15, except PD-L1 and IFNα as compared to the NTC. Moreover, following IFN-γ treatment, expression of type I interferon-response genes by quantitative real-time PCR analysis was further increased in the Chek2 KO as compared to the NTC cells (Fig. 3h). This data is suggestive of a negative correlation between Chek2 expression and response to IFN-γ signaling.

a UMAP graph of all single cells, showing cell annotation for macrophages (blue), tumor cells (orange), oligodendrocytes (green) and T cells (purple). Each dot represents an individual cell, n = 7930 cells, from 28 human glioblastoma tumor samples. b UMAP plot showing the expression of CHEK2 in macrophages, tumor cells, oligodendrocytes, and T cells. c Violin plot comparing the expression of CHEK2 in macrophages, tumor cells, oligodendrocytes and T cells. d Violin plot (right) of the gene signature scores of Interferon γ signaling in n = 94 T cells from high CHEK2 vs low CHEK2 expressing cases. e UMAP (left) and violin plot (right) of the gene signature scores of Interferon type I response in n = 6,863 tumor cells with high and low expression of CHEK2. Median of the frequency of CHEK2 expression (CPM > 0) inside the tumor cell compartment was used to dichotomize the 28 samples. The scRNA-seq data was used from the study published by Neftel et al.28. For (d and e), the p value represents two-tailed Mann–Whitney test; whiskers represent minimum and maximum values, the white dot inside the box represents the median and the box extends from the 25th to 75th percentiles. f Representative western blot showing knockout of Chek2 in GL261 cells. Histone H3 is used as a loading control. N = 3 independent replicates. g Flow cytometry analysis showing surface expression of PD-L1 on GL261 Chek2 KO and non-targeting control (NTC) clones, at the basal level and upon stimulation with IFNγ for 48 h. The histogram shows mean ± SE of one representative experiment of 3 independent experiments. p = 0.0034 by two-sided t test. h Quantitative real-time PCR analysis showing mean mRNA expression of PD-L1, IFN-α, IFN-β, IRF7 and ISG15 at the basal level and upon stimulation with IFN-γ for 48 h. GAPDH mRNA expression was used to normalize the expression of the target gene. Histograms represent mean ± SD, p value corresponds to p adjusted. N = 2 independent experiments with 4 technical replicates/independent experiment. Differences among cell types were evaluated using one-way ANOVA with post hoc Tukey’s multiple comparisons test. GL NTC in the figure corresponds to GL261 NTC and GL Chek2 KO corresponds to GL261 Chek2 KO. Source data for f–h are provided as a Source Data file.

Tumor cell intrinsic CHEK2 inversely correlates with antigen presentation pathway in human GBMs and impairs this pathway in mouse glioma cells

The scRNA-seq data of GBM patients28 showed enrichment of antigen presentation pathway in low CHEK2 expressing tumor cells as compared to the high CHEK2 expressing tumor cells (p = 3.2e−08; Fig. 4a). Correspondingly, the T-cell compartment associated with the low CHEK2 expressing tumor cells showed GO term enrichment for “T-cell-mediated cytotoxicity pathway” as compared to the T cells from high CHEK2 expressing tumor cells (p = 0.042; Fig. 4b) (Supplementary Data 1). Moreover, in the Abdelfattah et al.29 scRNA-seq data we also observed an a gene signature enrichment of the antigen presentation pathway (p = 1e−11) in low CHEK2 expressing tumor cells as compared to the high CHEK2 expressing tumor cells (Supplementary Fig. 3c). Further, the T-cell compartment associated with the low CHEK2 expressing tumor cells showed significant enrichment in pathway related to T-cell proliferation (p = 5.1e−17) as compared to the T cells from high CHEK2 expressing tumor cells (Supplementary Fig. 3d). Thus, CHEK2 expression in tumor cells was associated with consistent differences in the phenotype of T cells in GBM.

a UMAP (left) and violin plot (right) of the gene signature scores of antigen processing and presentation pathway in n = 6,863 tumor cells high and low CHEK2 expression from 28 human glioblastoma tumor samples. b Violin plot of the gene signature scores of T-cell-mediated cytotoxicity pathway in n = 94 T cells from high CHEK2 vs low CHEK2 expressing samples. The scRNA-seq data was used from the study published by Neftel et al.28. For (a and b), the p value represents two-tailed Mann–Whitney test; whiskers represent minimum and maximum values, the white dot inside the box represents the median and the box extends from the 25th to 75th percentiles. c Schematic showing the assay design to test the ability of Chek2 KO cells to promote OT-I CD8+ T-cell activation, assessed by cell proliferation (expansion index). d Flow cytometry analysis showing surface expression of MHCI-SIINFEKL on GL261 Chek2 KO and non-targeting control (NTC) clones, at the basal level and upon stimulation with IFNγ for 48 h. The histogram shows mean ± SD of one representative experiment of 3 independent experiments. p = 0.0059 by two-sided t test. e Four samples-NTC, Chek2 KO, NTC + IFNγ and Chek2 KO + IFNγ were cultured in individual well of the 6-well plates in triplicates. Each of the replicates of these treatment groups were cocultured with CD8+ T cells and OT-I CD8+ T cells independently (N = 3/experimental condition). WT and OT-1 CD8+ T-cell proliferation in all 24 samples was assessed by eFluor 450 fluorescence dilution individually. For NTC + IFNγ and Chek2 KO + IFNγ groups, the respective clones were stimulated with IFNγ for 48 h prior to culturing with WT CD8+ T cells or OT-I CD8+ T cells. Histograms represent mean ± SD of N = 3 replicates/condition. p values by two-way ANOVA. Source data for d, e are provided as a Source Data file.

As in the case of CHEK2, glioma cell expression of ATM, the upstream activator of CHEK2, also exhibited a T-cell phenotype similar to CHEK2 expression. The T-cell compartment associated with the low ATM expressing tumor cells showed GO term enrichment for “Interferon γ (IFN-γ) signaling” (p = 0.00019) and “T-cell-mediated cytotoxicity pathway” (p = 0.017) as compared to the T cells from high ATM expressing tumor cells (Supplementary Fig. 5a–c).

Unlike CHEK2, the T-cell compartment associated with the low CHEK1 expressing tumor cells showed no significant enrichment for “Interferon γ (IFN-γ) signaling” (p = 0.76) and “T-cell-mediated cytotoxicity pathway” (p = 0.18) as compared to the T cells from high CHEK1 expressing tumor cells (Supplementary Fig. 6a–c). Further, to investigate whether these correlations seen in human GBM patients have a causal relationship, we transfected the antigen reporter ovalbumin into both NTC and Chek2 KO cells in mouse glioma cell line GL261. As shown in Fig. 4c (Supplementary Fig. 7), we evaluated the ability of Chek2 depleted glioma cells to present MHCI restricted OVA peptide-SIINFEKL and activate SIINFEKL-specific OT-I CD8+ T cells. We found the enhanced presentation of MHCI-bound SIINFEKL peptide on the surface of IFN-γ treated Chek2 KO glioma cells as compared to NTC cells (Fig. 4d). Concomitantly, IFN-γ treated Chek2 KO glioma cells induced enhanced proliferation of OT-I CD8+ T cells as compared to the NTC cells (“OT-I CD8 T” panel, Fig. 4e). This phenomenon was predominantly dependent on antigen presentation, as IFN-γ treated Chek2 KO glioma cells failed to activate CD8+ T cells from WT C57BL/6 mice (“WT CD8 T” panel, Fig. 4e) (Supplementary Fig. 8).

Besides the immune-modulatory function of Chek2 demonstrated here, Chek2 is known to be involved in the DNA-damage response (DDR) and inhibition of Chek2 impairs DNA-repair pathways31. We investigated the changes in the induction of DNA-damage response using phosphorylation of γH2A.X as an indicator of DNA-damage response. Chek2 KO showed no difference in the phosphorylation of γH2A.X at the baseline as compared to the NTC cells (Supplementary Fig. 9a). Next, to investigate if Chek2 KO led to an increase in point mutations that could lead to the generation of more neoantigens, paired RNA and exome sequencing was performed for Chek2 KO and NTC cells (Supplementary Fig. 9b). Chek2 and Laptm4b were the only two genetic alterations predicted to have high binding affinities to MHC class I and gene expression (FPKM > 1) (Supplementary Fig. 9c, d). Identification of the Chek2 gene as one of the predicted neoantigens was expected since CRISPR cas9 KO of Chek2 introduces a frameshift mutation leading to generation of truncated transcript that is detected as a neoantigen. Contribution of Laptm4b to Chek2-related immune response needs further investigation.

Chek2 depletion/inhibition is associated with the STING pathway activation and upregulation of surface levels of PD-L1

Next, we investigated the mechanism/s underlying the enhanced gene expression of antigen presentation and type 1 interferon (IFN1) in glioma cells associated with Chek2 loss. We found that Chek2 inhibition/depletion resulted in activation of the STING pathway, as marked by the phosphorylation of TBK1 and upregulation of PD-L1 surface levels in GL261 and NPA glioma cell lines (Fig. 5a–g) (Supplementary Fig. 10). Further, we found a positive correlation between STING pathway and T-cell infiltration in the TCGA GBM dataset (Fig. 5h). These results suggested that Chek2 depletion/inhibition in tumor cells activates STING pathway and upregulates PD-L1 expression therefore, we hypothesized that Chek2 depletion/inhibition will sensitize gliomas to PD-1 or PD-L1 blockade, given that these forms of immunotherapy are thought to involve activity of effector T cells (Fig. 5i).

a Western blot showing the phosphorylation of TBK1 in GL261 Chek2 KO and non-targeting control (NTC) clones and in GL261 glioma cells treated with Chek1/Chek2 inhibitor Prexasertib (300 nmol/L) at the indicated time points. Total TBK1/β-Actin is used as a loading control. N = 3 independent replicates. Flow cytometry analysis showing surface expression of b, PD-L1 on GL261 Chek2 KO and NTC clones, at the basal level and upon stimulation with IFNγ for 48 h and c, PD-L1 on GL261 glioma cells treated with Prexasertib (300 nmol/L) at the indicated time points. d Western blot showing knockout of Chek2 in NPA cells. Histone H3 is used as a loading control. Western blot showing the phosphorylation of TBK1 in e, NPA Chek2 KO and NTC clones and in NPA glioma cells treated with Chek1/Chek2 inhibitor Prexasertib (300 nmol/L) at the indicated time points. Total TBK1 is used as a loading control. For d–e, N = 3 independent replicates. Flow cytometry analysis showing surface expression of f, PD-L1 on NPA Chek2 KO and NTC clones, at the basal level and upon stimulation with IFNγ for 48 h and g, PD-L1 on NPA glioma cells treated with Prexasertib (300 nmol/L) at the indicated time points. h Scatter plot showing correlation between T-cell infiltration and cytosolic DNA sensing-STING pathway in TCGA GBM dataset (n = 167 samples, two-tailed, Pearson correlation coefficient r = 0.53, p = 1.9e−13). i Proposed model: Chek2 depletion or pharmacological inhibition results in STING pathway activation and PD-L1 upregulation, which may sensitize gliomas to checkpoint blockade therapy. For b, c, f and g 10,000 cells/condition were analyzed over 3 independent experiments (mean ± SD, unpaired two-tailed t-test). Source data for Fig. a–g are provided as a Source Data file.

Chek2 depletion sensitizes gliomas to PD-1 blockade immunotherapy

Given the immune evasive function of Chek2 in glioma cells, we investigated whether Chek2 expression could influence glioma response to PD-1 blockade. We first confirmed that the WT GL261 line implanted into immune-competent mice, did not respond to PD-1 blockade (Fig. 6a, b). Next, we performed a similar survival study with GL261 NTC and Chek2 KO glioma cells. No statistically significant improvement in survival was seen in mice implanted with GL261 NTC controls treated with anti-PD-1 as compared to IgG treated animals (p = ns, Fig. 6c). Significant improvement in survival was seen in mice injected with GL261 Chek2 KO gliomas treated with anti-PD-1 as compared to IgG treated animals with 30% long-term survivors (LTS) (p < 0.05, Fig. 6d). The LTS animals (n = 3) in the anti-PD-1 group that were rechallenged with the same cells, on the contralateral hemisphere, showed no tumor growth as compared to the control animals (Fig. 6e). Overall, genetic depletion of Chek2 in tumor cells showed a modest improvement in response to PD-1 blockade in mouse GL261 glioma model.

a Dosing scheme for the PD-1 blockade treatment in mice with intracranially implanted GL261 glioma cells. b Kaplan–Meier (KM) survival curves of the C57BL/6 mice bearing GL261 wild-type cells. One group of mice (n = 7/group) were treated with the anti-PD-1 antibody and the other group of mice was treated with the isotype control antibody. 200,000 GL261 cells were injected/mouse. The median survival: IgG, 18 days; anti-PD-1, 18 days; Statistics: anti-PD-1 versus IgG, p = 0.59. c KM survival curves of the C57BL/6 mice bearing GL261 NTC (non-targeting control) cells. One group of mice (n = 7/group) was treated with the anti-PD-1 antibody, and the other group of mice (n = 10/group) was treated with the isotype control antibody. 50,000 GL261 NTC cells were injected/mouse. The median survival: IgG, 25 days; anti-PD-1, 29.5 days; Statistics: anti-PD-1 versus IgG, p = 0.07. d KM survival curves of the C57BL/6 mice bearing GL261 Chek2 KO cells. One group of mice (n = 10/group) was treated with the anti-PD-1 antibody, and the other group of mice was treated with isotype control antibody. 50,000 GL261 Chek2 KO cells were injected/mouse. The median survival: IgG, 20 days; anti-PD-1, 26 days; Statistics: anti-PD-1 versus IgG, p = 0.01. e The LTS in the Chek2 KO implanted group were rechallenged 80 days after the first implantation of Chek2 KO cells in the contralateral hemisphere with same type of cells and monitored. KM survival curves of the LTS mice from isotype and anti-PD-1 groups along with the control mice group which were implanted for the first time. The median survival: naive controls (6 mice), 21 days; isotype (1 mouse), undefined; anti-PD-1 (3 mice), undefined. Statistics: naive control versus isotype, p = 0.11; naive control versus anti-PD-1, p = 0.01. On the figure LTS is long-term survivors and anti-PD-1 is PD-1 blockade. Survival analysis for b–e was performed using the log-rank test. Statistical significance on the figure is depicted as ns: not statistically significant, *p < 0.05. Source data for b–e are provided as a Source Data file.

Pharmacological inhibition of Chek1/2 sensitizes gliomas to PD-1 blockade

To explore the translational potential of Chek2 modulation in the context of anti-PD-1 immunotherapy in gliomas, the blood-brain barrier permeable Chek1/2 inhibitor Prexasertib (LY2606368) which is being evaluated in clinical trials32,33,34,35, was tested in vivo. Compared to the vehicle control group, the combination of Prexasertib and anti-PD-1 conferred a significant extension of survival and 30% of animals were cured of glioma (Fig. 7a, b). Rechallenging of the LTS animals with tumor cells implanted in the contralateral hemisphere led to the rejection of the glioma, indicating induction of immunological memory and surveillance (Fig. 7c). Immunophenotyping of the rechallenged LTS animals showed an increase in the percentage of CD8 T-cell population expressing pro-inflammatory cytokines IFN-γ and TNF-α as compared to control tumor-bearing mice, in the brain compartment (Fig. 7d). Furthermore, splenic CD8 T cells from the LTS animals, treated with the combination therapy, expressed significantly higher levels of both IFN-γ and TNF-α, relative to non-tumor-bearing and control tumor-bearing mice (Fig. 7e). No such trend was seen in the CD8 T cells from deep cervical lymph nodes (dCLN) of the LTS animals treated with the combination therapy as compared to the non-tumor-bearing and control tumor-bearing mice (Fig. 7f). Similarly, we performed a survival experiment using ATM inhibition (AZD1390) in combination with PD-1 blockade. Survival of mice treated with combination of ATM inhibition and PD-1 blockade was similar to PD-1 blockade alone, but in contrast to the survival study using Prexasertib, no differences in the fraction of LTS was observed between these groups (Supplementary Fig. 11). Overall, our findings based on CRISPR screen, survival studies with PD-1 blockade, and scRNA-seq analysis suggest that the immune-modulatory phenotype of the ATM/Chek2 pathway is most robust when Chek2 is directly targeted as opposed to indirect, upstream targeting.

a The schematic representation of the dosing scheme for the survival experiment. b KM survival curves for C57BL/6 mice bearing GL261 glioma. Seven days after intracranial tumor implantation, the animals were randomized into 4 groups (10 animals/group): vehicle and isotype control (IgG), anti-PD-1, Prexasertib (Chek1/2 inhibitor), and Prexasertib and the anti-PD-1 combination group. The median survival duration in the treatment groups were as follows: VC + IgG, 24.5 days; VC + anti-PD-1, 25 days; Prexasertib + IgG, 24 days; Prexasertib + anti-PD-1, 43.5 days. Statistics: VC + IgG vs VC + anti-PD-1, p = 0.09; VC + IgG vs Prexasertib + IgG, p = 0.5; and VC + IgG vs Prexasertib + anti-PD-1, p = 0.01. c, KM survival curves for long-term survivors and naive controls that were rechallenged in the contralateral hemisphere. The median survival in the treatment groups were as follows: naive controls (9 mice), 21 days; VC + anti-PD-1 (1 mouse), undefined; Prexasertib + anti-PD-1 (3 mice), undefined. Statistics: naive control vs VC + anti-PD-1, p = 0.09; naive control vs Prexasertib+ anti-PD-1, p = 0.0062. Survival analysis for b, c was performed using the log-rank test. d Analysis of CD8 T-cell phenotype in the long-term survivors (LTS). Freshly dissected brains from Prexasertib + anti-PD-1 group, naive control (no tumor), and glioma-bearing control mice were analyzed for CD8 T-cell phenotype using flow cytometry. e Analysis of CD8 T-cell phenotype in the long-term survivors. Freshly dissected spleens from Prexasertib + anti-PD-1 group, naive control (no tumor), and glioma-bearing control mice were analyzed for CD8 T-cell phenotype using flow cytometry. f Analysis of CD8 T-cell phenotype in the long-term survivors. Freshly dissected deep cervical lymph nodes (dCLN) from Prexasertib + anti-PD-1 group, naive control (no tumor), and glioma-bearing control mice were analyzed for CD8 T-cell phenotype using flow cytometry. Statistical significance on the figure is depicted as ns: not statistically significant, *p < 0.05. Source data for b, c are provided as a Source Data file.

Combination therapy of Chek1/2 inhibition and PD-1/PD-L1 blockade shows efficacy in the context of standard-of-care radiotherapy

Radiation is a standard of care for glioblastoma patients. The Chek1/2 inhibitor AZD7762 was selected for these experiments since it has shown efficacy in the context of radiation therapy36. We used genetically engineered model derived cells called as NPA-with genetic features (NRAS, shP53, shATRX, wt-IDH1)36 that resembles human gliomas37,38. First, we tested the responsiveness of this model to PD-1 and PD-L1 blockade and found no significant improvement in survival (Supplementary Fig. 12a, b). The combination of AZD7762 + anti-PD-1 with standard-of-care radiotherapy and AZD7762 + anti-PD-L1 with standard-of-care radiotherapy showed significant extension of survival compared to the radiotherapy treated vehicle control group, with 30% of mice showing tumor eradication (Fig. 8a, b). Similar findings were observed in GL261 tumor-bearing mice showing a significant extension of survival as compared to the radiotherapy treated vehicle control group with 40% of mice showing tumor eradication in combination of radiation+AZD7762 + anti-PD-1 group and 60% of mice showing tumor eradication in combination of radiation+AZD7762 + anti-PD-L1 group (Fig. 9a, b). Radiotherapy alone was able to extend the survival of GL261 glioma-bearing mice by 10 days (Supplementary Fig. 13). To ascertain the role of CD8 T cells in mediating therapeutic response, the same treatment regimen was used in GL261 tumor-bearing CD8 KO mice. There was no significant difference in survival between the combination group of radiation+AZD7762 + anti-PD-1 as compared to the other groups, in CD8 KO mice (Fig. 9c), attributing the therapeutic effect seen in radiation+AZD7762 + anti-PD-1 combination therapy, to CD8 T cells.

a The schematic representation of the dosing scheme followed for the survival experiment. b (Left) KM survival curves for NPA glioma-bearing C57BL/6 mice. Seven days after intracranial implantation, all the animals were irradiated with 3 Gy whole-brain radiation for 3 consecutive days. 9 days after tumor implantation, the animals were randomized into 4 groups: vehicle control (n = 6), anti-PD-1 (n = 5), AZD7762 (Chek1/2 inhibitor) (n = 12) and AZD7762 + anti-PD-1 (n = 10) combination group. Survival analysis was performed using the log-rank test. The median survival duration in the treatment groups were as follows: Rad + VC + IgG, 24.5 days; Rad + VC + anti-PD-1, 32 days; Rad + AZD7762 + IgG, 28 days; Rad + AZD7762 + anti-PD-1, 39.5 days. Statistics: Rad + VC + IgG vs Rad + VC + anti-PD-1, p = 0.02; Rad + VC + IgG vs Rad + AZD7762 + IgG, p = 0.09; and Rad + VC + IgG versus Rad + AZD7762 + anti-PD-1, p = 0.018. (Right) KM survival curves for NPA glioma-bearing C57BL/6 mice. 7 days after intracranial implantation, all the animals were irradiated with 3 Gy whole-brain radiation for 3 consecutive days. 9 days after tumor implantation, the animals were randomized into 4 groups: vehicle control (n = 6), anti-PD-L1 (n = 6), AZD7762 (Chek1/2 inhibitor) (n = 12) and AZD7762 + anti-PD-L1 (n = 9) combination group. Survival analysis was performed using the log-rank test. The median survival duration in the treatment groups were as follows: Rad + VC + IgG, 24.5 days; Rad + VC + anti-PD-L1, 25.5 days; Rad + AZD7762 + IgG, 28 days; Rad + AZD7762 + anti-PD-L1, 35 days. Statistics: Rad + VC + IgG vs Rad + VC + anti-PD-L1, p = 0.32; Rad + VC + IgG vs Rad + AZD7762 + IgG, p = 0.09; and Rad + VC + IgG versus Rad + AZD7762 + anti-PD-L1, p = 0.0065. Statistical significance on the figure is depicted as ns: not statistically significant, *p < 0.05, **p < 0.01. Source data for b are provided as a Source Data file.

Discussion

We describe Chek2 kinase as an immune modulating kinase that contributes to tumor cell evasion from CD8 T-cell cytotoxicity in gliomas. Chek2 as a target, was identified in this study using an in vivo kinome CRISPR KO screen, was the most depleted kinase in WT immune-competent mice relative to CD8 KO mice. This suggested that tumor intrinsic Chek2 may be regulating resistance to CD8 T-cell-mediated cytotoxicity. Our mechanistic studies and human correlative analysis supports our CRISPR screen-derived observation that targeting this kinase can lead to activation of T cells. Our study describes the potential therapeutic value of combining a Checkpoint kinase 1/2 inhibitor with immune checkpoint blockade in murine glioma models. This is relevant since clinical trials investigating the efficacy of immune checkpoint blockade in GBM have failed to show a survival benefit21,39,40,41,42.

ScRNA-seq analysis of GBM samples in two independent datasets showed negative correlation between tumor intrinsic Chek2 expression and type I interferon response and antigen presentation on tumor cells. A negative association was observed between tumor intrinsic Chek2 expression and IFN-γ signaling and cytotoxicity of T cells. Along these lines, type I interferon response and antigen presentation have been shown to promote anti-tumor CD8 T-cell responses43. Evaluation of immune-modulatory effects of the Chek1/2 inhibitor Prexasertib demonstrated an increased expression of T-cell activation related genes and decreased expression of immunosuppression-related genes in mouse head and neck squamous cell carcinoma44. Blosser et al. identified interferon alpha and gamma response genes as gene sets associated with resistance to Prexasertib, using transcriptome analysis of a pan-cancer cell line panel, sarcoma and neuroblastoma xenograft models45. We found that Chek2 inhibition or depletion results in STING pathway activation and upregulation of PD-L1 expression which perhaps leads to increased expression of type I IFN genes that mediates susceptibility to killing by CD8 T cells.

Chek1 and Chek2 have been previously found to be overexpressed in recurrent GBMs post radiotherapy46. As such, Chek2 expression might contribute to the ineffectiveness of immune checkpoint blockade in GBM patients. The canonical function of Chek2 is to participate in DNA-damage responses47 yet our Chek2 KO clones did not display any difference in DNA-damage response. However, our results suggest that the mechanism by which Chek2 inhibits anti-tumoral immunity maybe due to STING pathway inactivation and not due to modulation of DNA-damage response since we did not observe any difference in the frequency of neoantigen predictions or increase in somatic mutations upon depletion of Chek2. Although CRISPR screen targeting is associated with frameshift mutations and potential generation of neoantigens, it is unlikely that such possibility explains the immune-modulatory effects we observe with Chek2 loss, since 1. Both Chek2 KO and inhibition of Chek2 kinase activity (with pharmacological inhibitors) demonstrated the same effect on STING pathway activation and expression of PD-L1 in two different glioma cell lines. 2. IFN-γ treated Chek2 KO glioma cells induced enhanced proliferation of OT-I CD8+ T cells while it failed to activate CD8+ T cells from WT C57BL/6 mice. 3. At least 2/4 Chek2 guides targeting different regions on the Chek2 gene were significantly depleted in WT mice as compared to the CD8 KO mice in 2 independent CRISPR screens. This is unlikely to occur if the selection is due to neoantigen generation given the random DNA insertions and deletions associated with CRISPR KO.

In our study, pharmacological inhibition of Chek1/2 by Prexasertib and AZD7762, two different inhibitors proven safe in clinical trials, rendered gliomas susceptible to PD-1 blockade in glioma models. As such, combinatorial treatment strategies of Chek1/2 inhibitors with immune checkpoint inhibitors and radiation were evaluated in different cancers and were shown to have translational potential. In small cell lung cancer, Prexasertib was shown to enhance the response to PD-L1 blockade through STING-mediated T-cell activation48. Replication stress response defect genes with the accumulation of immunostimulatory cytosolic DNA are more likely to identify subjects who might benefit49. Pharmacological induction of replication stress responses with AZD7762 was shown to further expand the benefits of immune checkpoint blockade to more patients with other types of malignancies49. Also the Chek1/2 inhibitors used in this study target both Chek1 and Chek2. Whereas we cannot rule out the contribution of inhibition of Chek1 when using these drugs, all our experimental evidence based on CRISPR screen, CD8 T- cell proliferation assay, survival study and scRNA-seq analysis confirms the role of Chek2 inhibition/depletion in modulating CD8 T-cell phenotype and response to immune checkpoint blockade, whereas the results for Chek1 on the CRISPR screen and scRNA-seq analyses, did not suggest that this kinase is immune modulatory.

Our study raises the question of whether Chek2 modulation might enhance other forms of immunotherapies. Given that the discovery of Chek2 was a result of an unbiased CD8 T-cell screen, it would be worthwhile to test the therapeutic effect of Chek1/2 inhibition therapy in combination with other types of T-cell-based immunotherapies like Chimeric Antigen Receptor (CAR) T Cell, bispecific t cell engagers (BiTEs), B-cell vaccine (Bvax) immunotherapies in addition to CTLA-4 and PDL1 blockade therapies50,51. The combination of Chek2 inhibition therapy with oncolytic viral therapy can also be a rational approach, based on the premise that Chek2 depleted tumor cells perhaps resembled virus-infected cells, in the context of induction of type I interferon response. Hence it would be worthwhile to test if Chek2 inhibition improves the efficacy of oncolytic viral therapies, perhaps by amplifying the type I interferon signaling52. Along with Chek2, the CRISPR screen identified other kinases like Map3k7, TK1, Uck1, Nek6 and Nuak1 that might contribute to resistance to CD8 T-cell responses. These might warrant further investigation as potential targets to potentiate T-cell-based immunotherapies in GBM. Sensitivity conferring kinases like Rbks, Scyl1, Tbk1, Axl, Fgr, Prkch identified in this study can be further investigated in the context of predictive biomarkers for T-cell-based immunotherapies.

Our study has several limitations. Importantly, the causal, mechanistic experiments were performed using murine glioma models, and investigation of these mechanisms in human tumors is warranted. Indeed, while mouse models are useful for initial testing of novel therapeutics, they may fail to fully recapitulate the biology of human GBMs and hence response to the therapy. In particular, a recent analysis of 3 GBM tumors revealed absence of STING expression, in the context of promoter methylation of this gene53. Given that publicly available GBM scRNA-seq datasets show heterogeneous expression of STING in tumor cells across patients28,29, how STING promoter methylation relates to activation of STING pathway when CHEK2 is lost, remains to be determined.

In summary, this work proposes a rationale and preclinical evidence to combine Checkpoint 1/2 inhibition with PD-1/PD-L1 blockade, to enhance the responsiveness of gliomas to immune checkpoint blockade therapy.

Article classification: Biological abstract

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