Transcriptional regulation of protein synthesis by mediator kinase represents a therapeutic vulnerability in MYC-driven medulloblastoma

Dong Wang; Caitlin Ritz; Yuhuan Luo; Ammu Suresh; Angela Pierce; Bethany Veo; Breauna Brunt; Nathan Dahl; Natalie Serkova; Sujatha Venkataraman; Etienne Danis; Kamil Kus; Milena Mazan; Tomasz Rzymski; Rajeev Vibhakar

doi:10.1038/s41467-025-64937-3

. 2025 Dec 16;16:11152. doi: 10.1038/s41467-025-64937-3

Transcriptional regulation of protein synthesis by mediator kinase represents a therapeutic vulnerability in MYC-driven medulloblastoma

Dong Wang ^1,², Caitlin Ritz ¹, Yuhuan Luo ³, Ammu Suresh ¹, Angela Pierce ^1,², Bethany Veo ^1,², Breauna Brunt ¹, Nathan Dahl ^1,², Natalie Serkova ⁴, Sujatha Venkataraman ^1,², Etienne Danis ⁵, Kamil Kus ⁶, Milena Mazan ⁶, Tomasz Rzymski ⁶, Rajeev Vibhakar ^1,^2,^7,^✉

PMCID: PMC12708665 PMID: 41402279

Abstract

MYC-driven medulloblastoma (MB) is a highly aggressive brain tumor with poor prognosis and limited treatment options. Through CRISPR-Cas9 screening, we identify the Mediator-associated kinase CDK8 as a critical regulator of MYC-driven MB. Both genetic loss and pharmacological inhibition of CDK8 impair MB tumor growth. Moreover, we find that CDK8 cooperates with MYC to sustain the MYC-mediated translational program, as CDK8 depletion induces pronounced transcriptional changes in translation-associated gene sets, reduces ribosome biogenesis, and impairs protein synthesis. Mechanistically, CDK8 regulates the occupancy of RNA polymerase II at specific chromatin loci, facilitating epigenetic alterations that promote the transcription of ribosomal genes. Furthermore, combined inhibition of CDK8 and mTOR synergistically enhances therapeutic efficacy in vivo, leading to more pronounced tumor growth suppression. Overall, our findings establish a functional link between CDK8-mediated transcriptional regulation and mRNA translation, suggesting a promising therapeutic approach targeting protein synthesis for MYC-driven MB.

Subject terms: CNS cancer, Chemotherapy, Ribosome

MYC-driven medulloblastoma is an aggressive pediatric tumor with limited treatment options. Here, the authors show that CDK8 regulates ribosome biogenesis and that combined inhibition of CDK8 and mTOR demonstrates therapeutic efficacy in mouse models of this cancer.

Introduction

Medulloblastoma (MB) is the most common malignant pediatric brain tumor, accounting for 15–20% of childhood brain tumors¹. Molecular profiling and genetic analysis categorized MB into four subgroups: WNT, SHH, Group 3, and Group 4^2,3. Among these, MYC-driven Group 3 MB (G3-MB) is associated with a high risk of relapse, often presenting with metastatic spread and local recurrence, leading to a long-term survival rate of approximately 50%^4,5. To date, targeted options for G3-MB tumors are lacking, in part because of the incomplete understanding of tumorigenic mechanisms.

Dysregulated expression of the MYC proto-oncogene contributes to the development of many types of human cancer⁶. Numerous studies have demonstrated that MYC plays a pivotal role in regulating protein synthesis^7–10. MYC affects cell proliferation, growth, and nucleolar size, and is associated with marked changes in the total rate of protein synthesis¹¹. It also regulates ribosome biogenesis either directly by upregulating ribosomal RNA and protein components through chromatin structure remodeling, or indirectly by controlling essential auxiliary factors involved in rRNA processing, ribosome assembly, and subunit transportation from the nucleus to the cytoplasm^7,9,12–14. Chromatin remodeling is an essential aspect of these processes through which MYC directly activates RNA polymerases^15–17. Understanding dysregulated protein synthesis in MYC-driven oncogenesis is crucial for developing targeted therapeutic interventions that leverage the inherent vulnerabilities of these pathways in the context of tumor development.

Cyclin-dependent kinase 8 (CDK8) associates with the mediator complex, a large multi-subunit complex that regulates transcription by connecting enhancer-bound transcription factors to RNA polymerase II^18,19. Overexpression of CDK8 has been demonstrated in various types of cancer, including colon cancer, breast cancer, glioblastoma, and hepatocellular carcinoma, making it a potential therapeutic target^20–23. Several studies have determined the efficacy of CDK8 inhibitors in preclinical cancer models^24–28. Importantly, unlike other transcriptional CDKs, CDK8 is not essential for basal transcription; instead, it plays a key role in driving transcriptional responses to stress and developmental stimuli^29–31.

In this study, we find that CDK8 is an essential gene for MB growth. Importantly, MYC-driven medulloblastoma exhibits the most significant susceptibility to the loss of CDK8 among all cancer types. We demonstrate that CDK8 depletion impairs protein synthesis and collaborates with MYC to promote tumorigenesis. Mechanistically, the loss of CDK8 induces pronounced transcriptional changes, resulting in the suppression of ribosomal gene expression and impeding the growth of MYC-driven MB. Furthermore, CDK8 inhibition with a selective inhibitor, RVU120, synergizes with mTOR inhibition to suppress MYC-driven MB²⁵. This work holds the promise of advancing our understanding of MYC-driven oncogenesis and provides critical preclinical data essential for the development of therapies targeting CDK8 and mTOR in MYC-driven medulloblastoma.

Results

CDK8 is a specific vulnerability in MYC-driven medulloblastoma

To systematically identify genes representing therapeutic vulnerabilities in MYC-driven MB, we performed CRISPR-Cas9 screening targeting 1140 druggable genes across three MYC-amplified human G3-MB cell lines (D425, D458, and D341)^32,33. CDK8 was identified as an essential gene for MB tumor growth (Fig. 1a, b and S1a). We next explored the significance of CDK8 by leveraging the Cancer Dependency Map (DepMap), a platform that utilizes gene knockout or knockdown to map gene dependencies across hundreds of cancer types³⁴. CDK8 is critical for various types of cancer, with MYC-driven medulloblastoma being the most sensitive cancer type to the loss of CDK8 (Fig. 1c). CDK8 stands out as a top dependency specific to medulloblastoma, similar to OTX2, NEUROG1, and NEUROD1—well-established genes that sustain stemness and drive proliferation in medulloblastoma (Fig. 1d)^35–38. Furthermore, CDK8 is the only gene with clinically relevant inhibitors among the top dependencies that are specific to MB, making it a potential target for the treatment of MB.

Fig. 1 — a Log-fold change in gene dependency from CRISPR-Cas9 screens in MB lines with TP53 as a positive control. b Read counts of sgRNAs targeting CDK8. Each dot represents an individual sgRNA. A decrease in read counts after puromycin selection indicates that CDK8 is selected as an essential gene. c DepMap lineage plot of CDK8 DEMETER2 scores across cancer types. n = cell lines per lineage. Lower scores indicate greater dependency. Boxes = Q1, median, Q3; whiskers = 1.5× IQR. Statistics from DepMap. d MB-specific gene-dependency t-statistics from DepMap; values < 0 denote essential genes. e UMAP analysis of cell clusters of single-cell RNA-seq data from G3-MB patient samples. A total of 12,595 cells were plotted after quality-control filtering. f CDK8 immunofluorescence staining. Data points from n = 3 biological replicates. Scale bar, 100 μm. Line indicates median. One-way ANOVA. g Proliferation of shNull/shCDK8 D425 (n = 4) and D458 (n = 5) cells. Technical replicates. h Images of neurosphere size in CDK8 knockdown and control cells are shown. n = 5 technical replicates. Scale bar, 400 μm. Barplot is for day 10. i Sphere formation and self-renewal measured by ELDA in shNull/shCDK8 MB lines. P values: likelihood ratio test. n = 5 biological replicates. j CDK8 knockout reduces D458 proliferation. n = 3 technical replicates. k Representative images of neurospheres formed by CDK8 knockout (n = 5) and control cells (n = 5). Scale bar, 200 μm. l Immunofluorescence staining of CDK8 in xenograft tumors from shNull or shCDK8 D458. White dashed lines = tumor boundary. n = 2 (shNull), n = 3 (shCDK8); one shNull mouse died before scan. Scale bar, 100 μm. Box plot line = mean. Two-sided t-test. m Representative MRI of xenografts. n = 2 (shNull), n = 3 (shCDK8). n H&E shows reduced tumor formation. Three mice per group euthanized at day 20. o Kaplan–Meier survival analysis of shNull (n = 6) and shCDK8 (n = 5) mice. Log-rank test.

CDK8 is a kinase that regulates gene expression by phosphorylating components of the Mediator complex. We reanalyzed single-cell RNA-seq data from seven patient samples previously reported by us and identified four main clusters within a total of 12,595 GP3 neoplastic cells³⁹. Genes associated with the Mediator Complex were expressed across all transcriptionally distinct cell clusters (Fig. 1e). In contrast, single-cell murine cerebellar transcript analysis showed relatively low expression of CDK8 and the Mediator Complex genes in normal tissues (Supplementary Fig. 1b). To further examine CDK8 in G3-MB, we performed immunofluorescence staining and observed elevated CDK8 expression in G3-MB cell lines (D458 and MB002) compared to normal human astrocytes (NHA) (Fig. 1f and Supplementary Fig. 1c, d). We then knocked down CDK8 in MB cells using lentivirus-mediated CDK8 shRNA. CDK8 depletion led to a marked reduction in cell proliferation (Fig. 1g) and significantly impaired neurosphere growth (Fig. 1h). Extreme limiting dilution analysis (ELDA) further demonstrated that loss of CDK8 reduced the self-renewal capacity and neurosphere-forming efficiency of MB cells (Fig. 1i). To further assess the dependency of MYC-driven MB on CDK8, we performed CRISPR-mediated knockout using CDK8-targeting sgRNAs, which similarly resulted in a significant reduction in both cell proliferation and neurosphere growth (Supplementary Fig. 1e–g and Fig. 1j, k). Next, we evaluated the in vivo role of CDK8 in tumor formation by implanting medulloblastoma cells transduced with either control shRNA (shNull) or CDK8-targeting shRNA (shCDK8) intracranially into immunodeficient mice. CDK8 knockdown suppressed tumor growth and prolonged the survival of tumor-bearing mice, as determined by MRI and immunohistochemistry, demonstrating that CDK8 is a critical regulator of MYC-driven MB growth (Fig. 1l–o and Supplementary Fig. 1h).

Targeting medulloblastoma with CDK8 inhibitor

We next examined the localization of CDK8 using two CDK8 antibodies across various MB cell lines, NHA, and a mouse embryonic fibroblast cell line (NIH3T3). Immunofluorescence analysis revealed predominant CDK8 expression within the nucleus (Supplementary Fig. 2a). Several small-molecule inhibitors targeting CDK8 are currently undergoing preclinical development^25–28. Our evaluation of eight CDK8 selective inhibitors demonstrated a broad range of half-maximal inhibitory concentrations (IC50) across three G3-MB cell lines. Among these, RVU120 demonstrated the greatest potency, exhibiting the lowest IC₅₀ values (Fig. 2a and Supplementary Fig. 2b). We further assessed the IC₅₀ of RVU120 across various MB and NHA cell lines. In G3-MB cells, the 72-hour IC₅₀ ranged from 125.90 to 866.70 nM, whereas NHA and SHH-MB cells showed substantially higher resistance, with IC₅₀ values around 4000 nM (Fig. 2b). Importantly, RVU120 treatment reduced the viability of patient-derived primary G3-MB cells, further confirming the efficacy of RVU120 in treating G3-MB (Fig. 2c).

Fig. 2 — a IC50 determination of various CDK8 inhibitors in MB cell lines. Unit: μmol. b IC50 of RVU120 at 72 h in MB and NHA cells. Experiments were performed in n = 3 independent experiments. All comparisons are to NHA cells. One-way ANOVA. c Dose-dependent proliferation curve of RVU120-treated primary MB cells from a G3-MB patient. n = 5 biological replicates. Mean ± SEM. One-way ANOVA. d Immunofluorescence of CDK8 and DAPI. MB cells were treated with 1000 nM RVU120 for 48 h. Data points from n = 3 biological replicates. Scale bar, 10 μm. The line on the box plot represents the median. Two-sided unpaired t-test. e Immunoblot analysis of p-STAT1 following time-dependent treatment with 1000 nM RVU120 across MB cell lines. Representative of n = 3 experiments. f Immunoblot analysis of p-STAT1 levels following dose-dependent RVU120 treatment at 72 h. Representative of n = 3 experiments. Mean ± SEM. Two-way ANOVA. g Methylcellulose assay in MB cells treated with RVU120. n = 3 biological replicates. Mean ± SEM. Two-way ANOVA. h Annexin V apoptosis assay. MB cells were treated with 1000 nM RVU120 for 48 h. n = 3 biological replicates. Mean ± SEM. Two-way ANOVA. i Identification of the brain tumor-initiating cell fraction in MB cells by ALDH expression demonstrates a decrease in the ALDH⁺ fraction following 1000 nM RVU120 treatment for 48 h. n = 3 biological replicates. Mean ± SEM. Two-way ANOVA. j Representative bioluminescence images of mice treated with RVU120 or vehicle (control, n = 8; RVU120, n = 6). k Kaplan–Meier survival curves of mice treated with vehicle (n = 8) or RVU120 (n = 6). Log-rank test. l Representative MRI of PDX411 xenograft mice treated with RVU120 or vehicle. Mice received the first scan after 14 days of treatment. Asterisks denote spongy tissue texture in mice. An adjusted texture analysis was performed to measure the tumor size. n = 3 mice. One control mouse died before the final scan. Mean ± SEM. Two-way ANOVA.

Treatment with RVU120 led to decreased CDK8 expression after 48 h of treatment and a concurrent reduction in p-STAT1 levels, which is a direct target of CDK8⁴⁰ (Fig. 2d–f and Supplementary Fig. 3a, b). Using a methylcellulose colony-forming assay and live cell imaging, we found that CDK8 inhibition suppressed colony formation and neurosphere growth in G3-MB cells (Fig. 2g and Supplementary Fig. 3c, d). Additionally, flow cytometry analysis revealed an increase in the total percentage of apoptotic cells following treatment with 1 μM RVU120, as determined by both annexin V and active caspase 3 staining using flow cytometry (Fig. 2h and Supplementary Fig. 3e). RVU120 treatment led to a reduction in neurosphere formation efficacy and the ALDH⁺ cell population, indicative of a decrease in the brain tumor-initiating cell fraction within a given cell population associated with stem-like properties such as self-renewal (Supplementary Fig. 3f and Fig. 2i). A similar effect was observed with another CDK8-selective inhibitor, BI1347 (Supplementary Fig. 3g, h).

To assess the potential intracranial efficacy of RVU120 in vivo, we evaluated its unbound partition coefficient, which determines the concentration of the compound in the CSF, corresponding to its free concentration in the brain. A ratio value of approximately 0.4 was observed, indicating permeation into the brain⁴¹ (Supplementary Fig. 3i). Furthermore, in the D458 injected MB xenograft model, we found that administration of 40 mg/kg RVU120 extended the survival of mice in the treatment group (Fig. 2j, k). In a patient-derived xenograft G3-MB model (PDX411), three-dimensional volumetric analysis of T2-turboRARE MRI sequences showed a significant decrease in tumor size after 14 days of RVU120 treatment compared with the control (Fig. 2l). Collectively, these findings reveal an oncogenic role of CDK8 in MB and indicate the therapeutic potential of RVU120 for the treatment of G3-MB.

CDK8 sustains MYC transcriptional activity in medulloblastoma

To further investigate CDK8 dependency in medulloblastoma, we analyzed DepMap data across MB cell lines and found that only high-MYC–expressing MB cells exhibit dependency on CDK8, whereas low-MYC cells do not (Fig. 3a). In contrast, its paralog CDK19 is not essential in either cell type. Using a cohort of 763 published MB samples along with normal cerebellar samples collected by us, we found that Group 3 MB exhibits higher CDK8 expression than normal cerebellum, particularly in the Group 3β and 3γ subtypes characterized by MYC overexpression (Fig. 3b). Kaplan–Meier survival analysis of the same dataset revealed that high CDK8 expression correlates with poorer overall survival in MYC-amplified MB (Fig. 3c). These findings suggest a cooperative role for CDK8 and MYC in driving tumor progression in Group 3 MB.

CDK8 has been shown to maintain stemness and tumorigenicity of glioma stem cells by regulating the c-MYC pathway²³. To examine whether CDK8 is a direct transcriptional target of MYC, we analyzed the correlation between CDK8 or CDK19 and MYC or MYCN expression in Group 3 and SHH MB, but found no significant positive association (Fig. 3d). Furthermore, MYC ChIP-seq in D458 cells revealed no direct MYC binding peaks at the promoters of CDK8 or CDK19 (Fig. 3e). Consistently, knockdown of MYC did not alter CDK8 expression (Fig. 3f). Together, these findings suggest that although MYC is a central transcriptional driver of G3-MB and CDK8 is highly expressed in MYC-driven tumors, CDK8 is not directly regulated by MYC at the transcriptional level.

We found that knockdown of CDK8 leads to a modest decrease in MYC expression (Fig. 3g). Despite CDK8 being a Mediator complex kinase known to broadly regulate gene expression, CDK8 knockdown resulted in more genes being upregulated than downregulated (Fig. 3h). Both knockdown and knockout of CDK8 led to reduced MYC protein levels in G3-MB cells (Fig. 3i, j). In contrast, treatment with the CDK8 kinase inhibitor RVU120 increased MYC protein levels (Supplementary Fig. 4a, b), possibly due to a compensatory response triggered by the loss of CDK8 kinase activity through stress signaling or transcriptional feedback. Importantly, both genetic depletion of CDK8 and pharmacologic inhibition with RVU120 significantly downregulated MYC-regulated target gene sets, as demonstrated by gene set enrichment analysis (Fig. 3k and Supplementary Fig. 4c). These findings suggest that CDK8 supports MYC-driven transcriptional programs and contributes to the maintenance of oncogenic gene expression in Group 3 MB. Targeting CDK8 may therefore impair the MYC transcriptional network and provide a therapeutic strategy for MYC-driven medulloblastoma.

CDK8 regulates protein synthesis in MYC-driven medulloblastoma

CDK8 depletion altered the hallmark features of MB, including neuronal differentiation, stemness, and photoreceptor cell maintenance (Supplementary Fig. 5a). Notably, many gene ontology (GO) terms related to mRNA translation and ribosome biogenesis were significantly decreased (Fig. 4a). Consistent with genetic depletion, RVU120 treatment led to the suppression of gene networks involved in protein synthesis, further confirming the specific inhibition of CDK8 by RVU120 (Supplementary Fig. 5b). Hyperactive ribosome biogenesis is a feature of MB, particularly in MYC-overexpressing Group 3 MB (3β and 3γ) (Fig. 4b). In MYC-driven cancer cells, including G3-MB cells, ribosomal genes are typically expressed at higher levels than most other genes (Fig. 4c). Upon CDK8 depletion or inhibition, numerous cytoplasmic and mitochondrial ribosomal genes were downregulated, suggesting that CDK8 plays a key role in regulating the transcription of ribosomal genes (Fig. 4d). Notably, CRISPR-mediated knockout of CDK8 led to a greater reduction in ribosomal gene expression compared to partial knockdown (Fig. 4e). These gene-level changes were enriched in pathways involved in ribosome assembly, ribonucleoprotein complex biogenesis, rRNA maturation, and rRNA modification (Fig. 4f). Strikingly, CDK8 knockout resulted in the top 10 gene ontology biological processes being exclusively related to ribosome biogenesis and mRNA translation (Fig. 4g).

Fig. 4 — a Alterations in GSEA gene sets observed in D458 cells following CDK8 knockdown. FDR from GSEA. b GSVA of patient samples (n = 763) showed enrichment of ribosome biogenesis gene sets in MYC-overexpressing Group 3β/3γ subtypes. c The expression of ribosomal genes was compared to that of all other genes in MB cells using RNA-seq analysis. d RNA-seq analysis demonstrated alterations in the expression of mitochondrial and cytoplasmic ribosomal genes following loss or inhibition of CDK8 in MB cells. n = 3 for each condition. P-values were FDR-adjusted. e RNA-seq of D458 cells: cytosolic ribosomal gene expression in shCDK8, sgCDK8, and control. n = 3 for each condition. f GSEA network showing downregulation of ribosome biogenesis in shCDK8 vs. shNull D458 cells. Node size reflects gene counts; edges indicate shared genes. g Top 10 GO biological processes are shown (n = 3 per condition; FDR-adjusted P values, GSEA). h Polysome profiling of lysates from CDK8 knockout or RVU120-treated (1 μM, 48 h) D458 cells reveals that CDK8 depletion reduces the ratio of polysome to sub-polysome compared to controls. i Immunofluorescence of Y10B and DAPI at 40X. MB cells were treated with the 1000 nM RVU120 for 48 h. Scale bar, 10 μm. Median line. n = 3 independent experiments. Two-sided t-test. j Immunofluorescence staining shows EU-incorporated RNA detected by Click-iT labeling. Scale bar, 100 μm. The line on the box plot represents the mean. n = 3 independent experiments. Kruskal-Wallis test. k CRISPR-mediated CDK8 knockout and control D458 cells were labeled with EU for 1 hour, and detected using the Click-iT reaction. Scale bar, 100 μm. Line indicates mean. n = 3 independent experiments. Two-sided Mann-Whitney test. l Flow cytometry analysis of OPP incorporation in MB cells treated with RVU120 at 1,000 nM versus control, quantified using FlowSight. Scale bar, 10 μm. Mean ± SEM. n = 3 independent experiments. Two-way ANOVA. m OPP assay showing protein synthesis in RVU120-treated MB cells compared to control cells. Scale bar, 100 μm. Line indicates mean. n = 3 independent experiments. Kruskal-Wallis test.

To explore the role of CDK8 in ribosome biogenesis, we performed polysome profiling and observed reduction in the polysome-to-subpolysome ratio following CDK8 depletion and inhibition, indicating impaired ribosome biogenesis (Fig. 4h). Supporting this, RVU120 treatment led to decreased levels of the ribosome biogenesis-associated proteins nucleolin (Ncl) and the rRNA methyltransferase fibrillarin (Fbl) (Supplementary Fig. 5c). Furthermore, RVU120 treatment led to a reduction in ribosomal RNA levels, as indicated by decreased Y10b immunostaining (Fig. 5i), suggesting a role for CDK8 in mediating rRNA synthesis. To further investigate this, we employed 5-ethynyl uridine (EU) labeling, a method that marks nascent RNA synthesis. CDK8 depletion or inhibition markedly reduced EU incorporation, indicating global transcriptional suppression, including rRNA synthesis (Fig. 4j, k and Supplementary Fig. 5d). These results collectively establish CDK8 as a key regulator of ribosome biogenesis by linking its activity to the coordinated transcription of ribosomal components and rRNA synthesis, which are essential for sustaining the biosynthetic demands of MYC-driven MB.

Fig. 5 — a Top 10 genes showing the highest correlation with MYC expression in Group 3 MB, based on analysis of the Cavalli dataset. n = 763. b GSEA of C5 GO gene sets comparing shMYC versus shNull in MB cells. P-values were adjusted for multiple testing using the Benjamini–Hochberg FDR in GSEA. n = 2 for each condition. c GSEA indicated alterations in GO biological process gene sets (FDR < 0.05) following the knockdown of CDK8, MYC, CDK7, CDK11, HNRNPH1, SOX11, or PLK1. n = 3 for each condition. d OPP assay in D458 and MB002 cells transfected with GFP-shRNA targeting MYC, followed by treatment with two doses of RVU120. Scale bar, 100 μm. The line on the box plot represents the median. n = 3 independent experiments. Kruskal-Wallis test. e OPP assay in D458 cells transfected with RFP-Omomyc, where RFP-positive cells mark Omomyc expression. Scale bar, 100 μm. The line on the box plot represents the median. n = 3 independent experiments. Two-sided Mann-Whitney test. f OPP assay in D458 cells transfected with RFP-Omomyc, followed by treatment with 1,000 nM RVU120. White arrows mark Omomyc-expressing (RFP-positive) cells. Scale bar, 100 μm. The line on the box plot represents the median. n = 3 independent experiments. Kruskal-Wallis test. g OPP assay showing dose-dependent effects of RVU120 treatment on ONS76 cells. Scale bar, 100 μm. The line on the box plot represents the median. n = 3 independent experiments. Kruskal-Wallis test. h OPP assay showing dose-dependent effects of RVU120 treatment on ONS76 cells transfected with c-MYC. Scale bar, 100 μm. The line on the box plot represents the median. n = 3 independent experiments. Kruskal-Wallis test. i IC₅₀ of RVU120 in parental ONS76 and DAOY cells compared to c-MYC–overexpressing cells. Mean ± SD. Two-sided unpaired t-test. Experiments were performed in three independent experiments, each with five biological replicates.

Building on this, we next asked whether the observed impairments in ribosome production translate to a functional reduction in protein synthesis. We employed an O-propargyl-puromycin (OPP) assay, which involves the introduction of a modified puromycin analog into cells, using click chemistry to visualize and quantify the rates of protein synthesis⁴². Treatment with RVU120 at 1 µM led to a time-dependent decrease in OPP signal from 1 to 48 h, indicating reduced protein synthesis (Fig. 4l, m). Cell viability remained largely unchanged, despite a substantial reduction in total cell number, suggesting that the decreased OPP signal was not due to nonspecific cytotoxicity (Supplementary Fig. 5e). Similarly, CDK8 knockdown phenocopied this reduction in translational activity (Supplementary Fig. 5f), confirming the role of CDK8 in regulating protein synthesis. Together, these findings establish CDK8 as a critical regulator that coordinates the transcriptional and translational machinery required for MYC-driven medulloblastoma.

Interplay between MYC and CDK8 in controlling protein synthesis

MYC plays a pivotal role in regulating mRNA translation and is a primary driver of ribosome biogenesis¹¹. In G3-MB, most ribosomal genes show a strong positive correlation with MYC expression (Fig. 5a). To examine whether the alterations in ribosomal genes were due to the loss of MYC, we examined gene set alterations following knockdown of MYC or other related genes (PLK1, CDK7, CDK9, SOX11, and HNRNPH1), all of which are known to suppress MB growth and affect MYC expression^32,33,43. Interestingly, MYC knockdown alone resulted in relatively few downregulated gene sets related to mRNA translation, with the most affected pathways instead linked to ATP synthesis and mitochondrial function (Fig. 5b). In contrast, CDK8 knockdown led to the most significant reduction in gene sets associated with mRNA translation and ribosome biogenesis (Fig. 5c). These findings support a critical role for CDK8 in sustaining protein synthesis in MYC-driven MB.

To assess whether CDK8-mediated regulation of protein synthesis depends on MYC, we performed OPP assays in MYC-MB cells following knockdown of MYC using specific shRNAs. Interestingly, CDK8 inhibition reduced OPP incorporation in MYC-deficient MB cells, suggesting that CDK8 contributes to protein synthesis even in the absence of high MYC activity (Fig. 5d). To explore this further, we employed Omomyc—a dominant-negative MYC inhibitor comprising the bHLHZip domain of MYC, which dimerizes with MYC and its binding partner MAX but prevents transcriptional activation by interfering with DNA binding. D458 cells transfected with Omomyc showed significantly reduced OPP signal, consistent with suppression of MYC-driven protein synthesis (Fig. 5e). RVU120 treatment further decreased OPP signal in Omomyc-expressing cells, indicating that CDK8 inhibition can suppress protein synthesis independently of MYC (Fig. 5f). Interestingly, cells with high MYC activity showed greater sensitivity to CDK8 inhibition, suggesting that protein synthesis in high MYC cells is more dependent on CDK8 (Supplementary Fig. 6a).

To further assess the dependence of CDK8-mediated protein synthesis on MYC, we performed OPP assays in SHH MB cell lines with low MYC expression (ONS76 and DAOY) treated with various doses of RVU120. A reduction in OPP signal was observed in DAOY cells only at 4 μM RVU120, while ONS76 cells showed no response, contrasting with the suppression observed at 0.5–1 μM in MYC-high cells (Fig. 5g and Supplementary Fig. 6b). We then overexpressed c-MYC in these SHH-MB cells and repeated the OPP assay (Supplementary Fig. 6c). Upon RVU120 treatment, MYC-overexpressing ONS76 showed a marked reduction in OPP signal at 4 μM (Fig. 5h). Additionally, the IC₅₀ of RVU120 was much lower in MYC-overexpressing SHH-MB cells compared to parental controls (Fig. 5i). The relatively modest response in DAOY cells may be attributed to their high baseline expression of CDK8 (Supplementary Fig. 6d). Collectively, these findings show that CDK8 inhibition suppresses protein synthesis in a MYC-independent manner, while MYC activity enhances the sensitivity of MB cells to CDK8 inhibition.

CDK8 transcriptionally regulates the expression of ribosomal genes

To determine whether CDK8 functions as a transcriptional activator affecting ribosomal genes, we performed a genome-wide analysis to map the occupancy of CDK8 and key histone markers using CUT&RUN in three G3-MB cell lines. CDK8 binding peaks were identified in both the promoter and enhancer regions (Fig. 6a, b). Gene annotation and functional enrichment analysis of these peaks revealed a strong enrichment for pathways associated with mRNA translation across all three cell lines (Fig. 6c). Notably, the genes driving this enrichment were primarily cytosolic and mitochondrial ribosomal genes, indicating that CDK8 regulates the transcription of ribosomal genes (Fig. 6d).

Fig. 6 — a Heatmaps showing CUT&RUN signals of CDK8, H3K4me3, H3K4me1, H3K27ac, BRD4, and MYC in D458 MB cells. The signals were displayed within a region spanning ± 3 kb around the transcription start site (TSS). n = 2 for each condition. b Pie chart showing CDK8 peaks are localized at promoter and enhancer. n = 2 for each condition. c Pathway enrichment analysis of CDK8 binding genes inferred from CUT&RUN. Translation pathways are enriched in MB cell lines. A total of 11,675, 14,909, and 12,895 genes were identified in D458, D425, and D283 cells, respectively. Statistical significance was assessed using Fisher’s exact test with the total number of genes in the genome as the background, and p-values were adjusted for multiple testing using the Benjamini–Hochberg FDR. d Venn-diagram showing overlapping of CDK8 binding genes associated with mRNA translation pathways. e Heatmaps displaying genome-wide binding CUT&RUN signals of CDK8 in CDK8 knockdown D458 cells compared to control cells. The signals are displayed within a region spanning ± 3 kb around the transcription start site (TSS). n = 2 for each condition. f Heatmaps displaying CUT&RUN signals of CDK8 and H3K4me3 in D458 cells with CDK8 knockdown compared to control cells at promoter regions. n = 2 for each condition. g Pathway enrichment analysis of genes associated with loss of H3K4me3 peaks (1207 genes). Statistical significance was assessed using Fisher’s exact test with the total number of genes in the genome as the background, and p-values were adjusted for multiple testing using the Benjamini–Hochberg FDR. h Heatmaps showing CUT&RUN signals of BRD4, H3K4me1, and MYC in D458 MB cells following CDK8 knockdown. n = 2 for each condition.

Upon CDK8 knockdown, we found a significant decrease in its genome-wide occupancy, predominantly in promoter regions, affecting genes associated with chromatin remodeling and mRNA translation pathways, indicating a role for CDK8 in the transcriptional regulation of mRNA translation (Fig. 6e and Supplementary Fig. 7a). Next, we assessed the occupancy of typical histone markers (H3K4me3, BRD4, H3K27me3, and H3K4me1). CDK8 depletion led to a significant loss of chromatin occupancy of H3K4me3 at promoter regions, which are essential for gene activation and the initiation of transcription (Fig. 6f and Supplementary Fig. 7b), as well as a slight change in H3K27me3 at the promoters (Supplementary Fig. 7c). These transcriptional alterations are associated with chromatin remodeling, nervous system development, and axon guidance pathways, resulting in changes to the chromatin landscape of transcription factors and neurogenesis in MB (Fig. 6g and Supplementary Fig. 7d). Interestingly, depletion of CDK8 led to an increased in CDK8, BRD4, and MYC signals at promoters or enhancers, suggesting that RNA Polymerase Pol II may experience promoter-proximal pausing following CDK8 depletion (Fig. 6f, h).

Therefore, we next performed CUT&RUN profiling using antibodies against RNA Pol II and phospho-Pol II. CDK8 knockdown leads to Pol II predominantly pausing at the promoter regions, while the decrease in phosphorylated Pol II extends from the 5’ to the 3’ end across the gene body (Fig. 7a, b and Supplementary Fig. 7e). Among the peaks showing at least a 1.5-fold change following CDK8 knockdown, we observed a greater than five-fold increase in Pol II-binding sites and a three-fold decrease in phospho-Pol II-binding sites (Supplementary Fig. 7f). Similar chromatin alterations in Pol II and phospho-Pol II were observed in both cytosolic and mitochondrial ribosomal genes following CDK8 knockdown (Fig. 7c). These chromatin changes were associated with ribosomal gene expression, as evidenced by the overlap peak track of CUT&RUN and RNA-seq (Fig. 7d). The differential alterations in Pol II and phospho-Pol II peaks significantly contributed to various pathways associated with mRNA translation, including rRNA metabolic processes and ribosome biogenesis (Fig. 7e).

Fig. 7 — a Heatmaps showing CUT&RUN signals of Pol II and phospho-Pol II in D458 cells with CDK8 knockdown compared to control cells at promoter regions. n = 2 for each condition. b Empirical cumulative distribution function (ECDF) plot shows significant increase in promoter-proximal pausing following CDK8 knockdown. n = 2 for each condition. c Average distribution and heatmaps of H3K4me3, Pol II, and phospho-Pol II signals on ribosomal genes. n = 2 for each condition. d Representative examples of Pol II and phospho-Pol II binding sites on ribosomal genes observed following CDK8 knockdown. n = 2 for each condition. e Enrichment analysis shows mRNA translation pathways enriched among genes with increased Pol II peaks (11,617 genes) or decreased phospho-Pol II peaks (7174 genes) following CDK8 knockdown. Statistical significance was assessed using Fisher’s exact test with the total number of genes in the genome as the background, and p-values were adjusted for multiple testing using the Benjamini–Hochberg FDR. f Immunoblot showing the levels of Pol II and phospho-Pol II in D458 cells following treatment with RVU120. Representative of n = 3 experiments. g Heatmaps showing CUT&RUN signals of RNA Pol II and phospho-RNA Pol II in D458 MB cells treated with 1,000 nM RVU120 for 48 h. n = 2 for each condition. h Average distribution of RNA Pol II and phospho-RNA Pol II peaks showing the alteration of RNA Pol II and phospho-RNA Pol II signals across the gene body following the treatment of RVU120. n = 2. i Average distribution and heatmaps of RNA Pol II and phospho-RNA Pol II signals on cytosolic and mitochondrial ribosomal genes following the treatment of RVU120. n = 2. j Representative examples of RNA Pol II and phospho-RNA Pol II binding sites on ribosomal genes observed following the treatment of RVU120. n = 2 for each condition.

To further delineate the role of CDK8 in transcriptional regulation, we examined the phosphorylation level of the RNA Pol II C-terminal domain (CTD) in MB cells. Pharmacological inhibition of CDK8 with RVU120 led to a reduction in CTD phosphorylation (Fig. 7f). Furthermore, CUT&RUN analysis following RVU120 treatment revealed reduced chromatin occupancy of both total and phospho-Pol II at promoter regions, likely due to impaired recruitment resulting from the complete loss of CDK8 kinase activity (Fig. 7g, h). CDK8 inhibition diminished Pol II and phospho-Pol II binding at the promoters of ribosomal genes (Fig. 7i), leading to decreased expression of these genes (Fig. 7j). Collectively, these findings indicate that CDK8 plays a critical role in the transcriptional regulation of ribosomal genes.

CDK8 regulates mTOR signaling in MYC-driven medulloblastoma

Given that our data implicated protein synthesis as a major target of CDK8, we further examined this process. Mammalian target of rapamycin (mTOR) plays a key role in protein synthesis by regulating translational initiation, elongation, and ribosome biogenesis. To explore the significance of mTOR signaling in MB, we performed gene set variation analysis on gene expression data from 763 MB patient samples⁴⁴. Our analysis revealed hyperactive mTORC1 signaling, mRNA translation, and MYC signaling in G3-MB cells (Fig. 8a). Subsequently, multiplex IHC was performed on G3-MB patient samples stained for CDK8, p-4EBP (T37/46), p-S6 (S235/236), p-AKT (S473), c-MYC, and RPS12. Consistent with our gene-level findings, the staining intensity of all these protein markers was significantly higher in G3-MB than in non-tumor control regions (Fig. 8b and Supplementary Fig. 8a, b). Although previous studies have suggested that targeting mTOR may be effective in medulloblastoma, most have focused on the SHH subtype or used dual PI3K-mTOR inhibitors^45–48. In a previous study of G3-MB, one-week treatment with the mTOR inhibitor TAK-228 did not improve survival in the D425 model but showed modest benefit in the MED211 model⁴⁹. To evaluate the therapeutic potential of mTOR inhibition in MYC-driven MB, we treated two G3-MB xenograft models with TAK-228, a second-generation mTOR inhibitor with demonstrated blood-brain barrier penetration. Daily oral administration of TAK-228 significantly prolonged survival in tumor-bearing mice compared to controls, indicating that targeting mTOR represents a promising strategy for MYC-driven G3-MB (Fig. 8c, d).

Fig. 8 — a Gene set variation analysis of patient samples (n = 763) revealed that the MYC-overexpressing subtypes Group3β and 3γ were enriched with gene sets of MYC and mTOR signaling. b Multiplex IHC on G3-MB patient samples using CDK8, p-4EBP1, c-MYC, RPS12, p-S6, and p-AKT antibodies. n = 3 patient samples were analyzed. For each antibody, 10 fields of view were captured, and the signal intensities from all points were combined for analysis. Top scale bar, 100 μm. Bottom scale bar, 10 μm. p < 0.0001 in all biomarker groups. Two-sided Mann–Whitney U. c Representative bioluminescence images of mice injected with D425 cells and treated with TAK-228 (1 mg/kg, daily, oral gavage) or vehicle control. Treatment was initiated upon tumor establishment and continued until endpoint. Kaplan–Meier survival curves show extended survival in the TAK-228–treated cohort compared to controls. Statistical significance was determined using the log-rank test. n = 5 for each group. d Representative bioluminescence images and Kaplan–Meier survival analysis of D458 xenograft-bearing mice treated with TAK-228 (1 mg/kg, daily, oral gavage), following the same protocol as in (c). TAK-228 treatment reduced tumor burden and significantly prolonged survival compared to the control group (log-rank test). n = 5 for each group. e GSEA plots of representative gene sets involved in mTOR signaling following CDK8 depletion or inhibition. Normalized enrichment score (NES) and false discovery rate (FDR) are indicated. n = 3 for each condition. f–h Immunoblot showing the levels of p-4EBP1 and p-S6 upon treatment with RVU120 in Group 3 medulloblastoma cells. n = 3 biological replicates. i Immunoblot showing the levels of p-4EBP1 and p-S6 following CDK8 knockout. n = 3 independent experiments. Mean ± SEM. Statistical analysis: two-way ANOVA. j Immunoblot showing the levels of p-mTOR and mTOR upon treatment with RVU120 for 24 h in D458 and MB002 cells. The data are representative of n = 3 independent experiments.

Gene set enrichment analysis of RNA-seq data from CDK8 genetic knockdown and pharmacological inhibition revealed significant downregulation of mTOR signaling-associated gene sets (Fig. 8e), suggesting that CDK8 mediates mTOR signaling. To further examine the role of CDK8 in this pathway, we evaluated the phosphorylation level of two key mTORC1 substrates: S6K1 and 4EBP1. Treatment with RVU120 led to a time- and dose-dependent decrease in phosphorylation of both proteins (Fig. 8f–h). Similarly, genetic knockout of CDK8 resulted in reduced phosphorylation of S6K1 and 4EBP1 (Fig. 8i). Notably, total and phospho-mTOR levels remained unchanged, suggesting that CDK8 modulates mTORC1 signaling independently of mTOR activation (Fig. 8j). These findings support a model in which CDK8 regulates mTOR signaling and contributes to the translational program in MYC-driven medulloblastoma.

Synergistic targeting of CDK8 and mTOR in MYC-Driven medulloblastoma

Given the similar impact of mTOR and CDK8 inhibitors on the suppression of protein synthesis in MB cells, we examined whether simultaneous inhibition of CDK8 and mTOR could synergistically impede the growth of MB cells. CDK8 knockdown cells showed reduced sensitivity to Torin1, an ATP-competitive inhibitor that blocks mTORC1 and mTORC2, as demonstrated by the lower IC50 compared to control cells (Supplementary Fig. 9a). Next, we performed a combination treatment study using increasing doses of RVU120 and Torin 1 or TAK228 on MB cells. Dual inhibition resulted in a significant synergistic effect on the lethality and proliferation of MB cells (Fig. 9a, b and Supplementary Fig. 9b, c). Subsequent evaluation using the Chou-Talalay method and Bliss synergy model confirmed this synergistic effect (Fig. 9c, d and Supplementary Fig. 9d). Flow cytometry analysis revealed that combination treatment enhanced the apoptosis of MB cells (Fig. 9e and Supplementary Fig. 9e). Consistent with these results, dual inhibition significantly decreased the levels of p-4EBP1 and p-S6 and reduced p-STAT1 and phospho-Pol II levels, further emphasizing the role of CDK8 in regulating both protein synthesis and chromatin dynamics (Fig. 9f, g).

Fig. 9 — a Dose-dependent assay of the combined treatment with RVU120 and Torin1 on day 5. Scale bar, 400 μm. n = 3 biological replicates. b Real-time proliferation assay quantifying the combined treatment with RVU120 and Torin1. Control (n = 9); treatment (n = 3) biological replicates. Mean ± SEM. All p < 0.0001 vs. control (*). Two-way ANOVA. c Heatmap representation of the Fraction Affected and the Bliss interaction index across the five-point dose range of RVU120 and Torin1. Mean values of n = 3 biological experiments are shown. d The combination index of RVU120 and Torin1 using Chou-Talalay method. The mean combination index was determined from n = 3 biological replicates. e Apoptosis assay following combined treatment with RVU120 and Torin1. MB cells were treated for 48 h before staining with PI and Annexin V. n = 3 biological replicates. Mean ± SEM. Two-way ANOVA. **f, g** Effects of the combination of RVU120 and Torin1 on protein synthesis markers, phospho-Pol2 and phospho-STAT1, in MB cells after 48 h of treatment. Representative of n = 3 experiments. h The nude mice injected with D458 cells were treated with vehicle (n = 10), RVU120 (40 mg/kg, n = 6), TAK-228 (1 mg/kg, n = 8), or their combination (n = 7). i Representative Sagittal T2-weighted turboRARE MRI of D458 xenografted mice at 22 days. White arrows indicate tumors. MRI volumetric analysis is shown. n = 2 mice were selected and scanned in each group. j Kaplan–Meier survival analysis of mice treated with vehicle control (n = 10), TAK-228 (n = 8), RVU120 (n = 6), or the combination of TAK-228 and RVU120 (n = 7). Statistical significance was assessed using the log-rank test.

To explore synthetic lethality in vivo, we assessed the efficacy of RVU120 and TAK-228 administered individually or in combination in xenograft model. Initially, the IVIS signals indicated similar tumor sizes in all groups. Tumor growth notably decelerated in the treated mice, particularly in the cohort that received the combination treatment (Fig. 9h and Supplementary Fig. 9f). The RVU120 and combination-treated groups showed decreased weight loss, possibly because CDK8 is necessary for intrinsic growth and differentiation of intestinal epithelial cells (Supplementary Fig. 9g). Mice receiving combination treatment showed the most effective therapeutic outcomes, characterized by prolonged overall survival and reduced tumor burden, as determined by MRI (Fig. 9i, j). Additionally, hematological analyses conducted prior to euthanizing the mice revealed that administration in each group did not induce notable acute hematological toxicity, as evidenced by the stable white blood cells, neutrophils, lymphocytes and other hematological parameters (Supplementary Fig. 9h). Taken together, these studies establish the therapeutic efficacy of combination treatment with mTOR and CDK8 inhibitors in vivo and in vitro, opening an alternate path for biologically based therapeutic trials in MYC-driven MB.

Discussion

Medulloblastoma is the most common and lethal pediatric brain tumor^1,50–52. It is crucial to identify disease vulnerabilities and develop therapies that target specific mechanisms. Here, we found MYC-driven medulloblastoma as one of the most significantly affected cancer types following CDK8 depletion, demonstrating the essential role of CDK8 in driving medulloblastoma growth. Our findings revealed a role of CDK8 in regulating protein synthesis. This expands on previous studies that identified a link between CDK8 and MYC, providing a mechanism by which CDK8 may facilitate MYC-driven tumorigenesis^23,53,54.

While CDK8 expression does not appear to be directly regulated by MYC, our findings indicate that MYC requires CDK8 to sustain the oncogenic transcriptional program essential for tumor growth. Inhibition of CDK8 disrupts MYC-driven transcriptional networks. This supports a model of non-oncogene addiction, in which cancer cells driven by major oncogenes such as MYC become highly dependent on non-oncogenic cellular processes that, although not directly involved in tumor initiation, are essential for sustaining survival and proliferation^55,56. Targeting these auxiliary pathways, such as CDK8-mediated transcriptional regulation, offers a promising therapeutic strategy to selectively impair the viability of MYC-driven tumors.

Dysregulation of protein synthesis is a common characteristic of MYC-driven cancers and is marked by increased Pol I-mediated ribosomal rDNA transcription and mTOR/eIF4E-driven mRNA translation^13,17. The mechanism by which dysregulation of protein synthesis contributes to cancer development and progression remains unclear. One possibility is that dysregulation of translation promotes cell growth, proliferation, and metastasis⁵⁷. This is supported by the observation that cancer cells frequently develop a strong addiction to protein synthesis to adapt to different microenvironments, providing a vulnerability that can be effectively targeted by inhibiting protein synthesis in these cancer types⁵⁸. Another possibility is that changes in translational dysregulation affect specific molecular or cellular processes that contribute to cancer initiation and progression^59,60. Studies have demonstrated that aberrant protein synthesis leads to changes in the expression of specific genes by affecting chromatin dynamics via epigenetic mechanisms^61,62. Our findings reveal that CDK8 depletion or inhibition led to marked transcriptional downregulation of cytoplasmic and mitochondrial ribosomal genes, suppression of rRNA synthesis, and impaired polysome formation, collectively resulting in reduced translational output. Subsequently, we investigated the mechanisms through which CDK8 regulates these cellular activities. As a dissociable part of the mediator complex, CDK8 inhibition results in decreased phosphorylation of RNA Pol II, consequently affecting the targeted suppression of gene expression, specifically of genes linked to ribosomal function. These results reveal a role for CDK8 in coupling transcriptional regulation to translational output in MYC-driven tumors.

Evidence suggests that MYC and mTOR signaling pathways converge to increase translational capacity, with MYC amplifying the transcription of key components that are subsequently activated by mTOR-mediated phosphorylation^63–65. This synergy enables cancer cells to meet the high biosynthetic demands required for tumor growth and survival⁶⁴. CDK8 may play a key role in facilitating this transcriptional activity—such as oncogene activation, rRNA synthesis, and ribosomal gene expression—thereby acting as a critical mediator of the MYC–mTOR axis. Disrupting the MYC–mTOR axis has shown promise in impairing tumor progression⁶⁶. Previous studies have demonstrated the robust efficacy of PI3K/mTOR inhibitors in inhibiting the growth of MB cells derived from MYC+DNp53 transfected stem cells, both in vitro and in vivo⁴⁸. Our data show that CDK8 inhibition reduced phosphorylation of key mTORC1 effectors—S6K1 and 4EBP1—without altering total mTOR levels, suggesting that CDK8 modulates mTOR signaling downstream of or in parallel with mTOR itself. This aligns with a previous study in acute lymphoblastic leukemia, which established a functional link between CDK8 and mTOR signaling, supporting a broader role for CDK8 in controlling protein synthesis across multiple cancer types. Our findings position CDK8 as a key node in the transcription-controlled translation axis and a promising therapeutic target in medulloblastoma and other MYC- or mTOR-activated malignancies.

In G3-MBs, approximately 17% of Group 3 MB cases demonstrate high-level MYC amplification, a defining characteristic contributing to widespread treatment failure in children diagnosed with MYC-amplified MB despite current therapies⁶³. MYC, which functions as a pleiotropic transcription factor, promotes the proliferation of neural progenitor cells in malignant stem cells by modulating overall gene expression and regulating critical cellular processes⁶⁴. Although MYC can drive cerebellar stem cell proliferation in vitro, it is insufficient to maintain long-term growth in animal models. A previous study revealed that cerebellar stem cells require both MYC overexpression and mutant Trp53 to generate aggressive MB upon orthotopic transplantation⁶⁵. Similar studies have demonstrated that the combination of MYC with GFI1 or MYC with SOX2 leads to rapid formation of highly aggressive cerebellar tumors using stem cells or astrocyte progenitors^67,68. Given the significant role of CDK8 in G3-MB identified in our study, it will be of great interest to determine in future studies whether CDK8 and MYC overexpression in cerebellar stem cells is sufficient to drive tumorigenesis and form G3-MB tumors.

We demonstrated a therapeutic strategy for targeting MYC-driven MB using RVU120, a specific and selective inhibitor of CDK8²⁵. RVU120 exhibits the necessary pharmacological properties, such as high oral bioavailability and brain penetration. A Phase 1 trial in patients with AML or high-risk MDS (RIVER51) showed good tolerability with acceptable toxicity and signs of clinical activity (NCT04021368). As of November 2023, 38 patients have been enrolled in the RIVER51 trial without any reported dose-limiting toxicities. Pharmacodynamic studies have demonstrated target engagement, with significant attenuation of CDK8-downstream biomarkers in peripheral blood monocytes and leukemic cells. Concurrently, Phase 2 studies RIVER-52 (NCT06268574) and RIVER-81 (NCT06191263) are underway, including a combination study of ruxolitinib and RVU120 in the POTAMI-61 study (NCT06397313). The finding that mTOR inhibition can combine with CDK8 inhibition significantly enhances the option for therapy. Targeting mTOR safely with everolimus has been extensively established in children. These clinical data support our studies and the concept of using RVU120 in pediatric medulloblastoma in combination with mTOR inhibition. A Phase1/2 trial testing this concept is under development through the Pediatric Brain Tumor Consortium (PBTC; C180). These studies will be an important step toward incorporating RVU120 into standard-of-care chemotherapy backbones in the future. In conclusion, our data suggest that the CDK8 inhibitor RVU120 is a promising agent for MYC-driven medulloblastoma therapy and provides a mechanistic basis for future research.

Methods

Brain samples collection

Written informed consent was obtained from all participants recruited for this study or, where applicable, from their legal guardians prior to participation in this study. The protocol of human subjects was approved by the ethics committee of the University of Colorado and Children’s Hospital Colorado (COMIRBs #95– 500).

Cell lines

The medulloblastoma cell line D425 was purchased from Millipore Sigma (SCC290). D458 was purchased from Cellosaurus (CVCL_1161), D283 from ATCC (HTB-185), and D341 from ATCC (HTB-187), respectively. MB002 was provided by Dr. Martine Roussel (St. Jude Children’s Research Hospital). HDMB03 was provided by Dr. Mahapatra of the University of Nebraska. Human astrocytes were cultured in complete Astrocyte Medium (ScienCell, 1801). MAF1433 cells were isolated and cultured from the primary tumor of a patient with G3-MB. The D425 and D458 cell lines were cultured in DMEM supplemented with 10% FBS, 1% 1× penicillin/streptomycin solution, 1% 1× L-glutamine, and 1% sodium pyruvate. D283 cells were cultured in DMEM (Thermo Fisher) supplemented with 10% FBS, 1 mM sodium pyruvate, 1× penicillin/streptomycin solution (Cellgro), and 1× nonessential amino acids (Millipore Sigma). HDMB03 cells were cultured in 90% RPMI 1640, 10% FBS, and 1× penicillin/streptomycin. D341 and MB002 were cultured in neurobasal medium (Sigma, SCM003) containing 2% B-27, 1 μg/ml heparin, 2 mM L-glutamine, 1% penicillin/streptomycin, 25 ng/ml fibroblast growth factor (FGF), and 25 ng/ml epidermal growth factor (EGF). All cell lines were cultured at 37 °C in 95% air and 5% CO₂. All cell lines tested negative for Mycoplasma. Cell proliferation assays and live-cell imaging were performed using an Incucyte SX5 Live-Cell Analysis System (Sartorius). All cell lines are periodically authenticated by DNA fingerprinting, and we will continue to fingerprint all cell lines every 6 months.

Transfection

shRNA vectors targeting CDK8 mRNA (TRCN0000350344 and TRCN0000382350) and a non-targeting control shRNA were obtained from the Functional Genomics Facility at the University of Colorado Anschutz Medical Campus. TRCN0000350344 was used for bulk RNA sequencing, CUT&RUN, and animal studies. The human MYC (NM_002467.6) expression construct was purchased from VectorBuilder (VB010000-9492agg). shRNAs targeting c-MYC were also obtained from VectorBuilder: shRNA#1 (VB241123-1086vdf), shRNA#2 (VB241123-1087jkp), and shRNA#3 (VB241123-1088rds). The corresponding vector control was VB010000-9526zpu. Viral transduction was performed as described previously^33,43.

Methylcellulose assay

2000 cells/3 mL were plated in a 1:1 mixture of 2.6% methylcellulose and complete growth medium. The cells were allowed to grow for two weeks. Colonies were stained with nitrotetrazolium blue chloride (Sigma) at 1.5 mg/mL in PBS for 24 h at 37 °C and counted.

Aldehyde dehydrogenase assay

ALDH activity was measured using an Aldefluor kit (Stem Cell Technologies), according to the manufacturer’s instructions. Briefly, 1 × 10⁵ cells were resuspended in 0.5 mL Aldefluor buffer, separated equally into two tubes, and 5 μl of DEAB reagent was added to one tube as a negative control. Then 1.25 μl of Aldefluor Reagent was added to each tube and mixed well. After incubation at 37 °C for 45 min and centrifugation, the cells were stained with propidium iodide and analyzed using a FlowSight Imaging Flow Cytometer (EMD Millipore).

Neurosphere assay

Medulloblastoma cells were grown for 14 days in neurosphere medium. The spheres were disassociated and replanted into 100-, 10-, and single-cell suspensions on day 14. The cells were grown for an additional 14 days with or without RVU120. The spheres were imaged using an Incucyte S3 Live Cell Imaging System (Sartorius).

Immunofluorescence

The cells were washed and seeded onto polylysine-coated slides, and then fixed with 4% paraformaldehyde for 15 min at room temperature, permeabilized with 0.2% Triton X-100 in PBS for 15 min, and incubated in 3% BSA diluted in 0.05% Triton X-100 for 30 min at room temperature on a shaker. After blocking, the cells were incubated with the primary antibodies. The following antibodies were used: phospho-4EBP1 (Santa Cruz Biotechnology, sc-293124, 1:50), CDK8 (Santa Cruz Biotechnology, sc-13155, 1:50), CDK8 (Abcam, ab224828, 1:200), CDK8 (Invitrogen, PA5-11500, 1:200), fibrillarin (Abcam, EPR10823, 1:200), nucleolin (Abcam, EPR7952, 1:200), and ribosomal RNA antibody Y10B (Abcam, ab171119, 1:200) for 1 h at room temperature. After washing with 0.05% Triton X-100, cells were incubated with Alexa Fluor 647-or Alexa Fluor 488-conjugated secondary antibody (1:500) for 1 hour at room temperature in the dark, washed with PBS, and mounted using ProLong Gold antifade reagent containing DAPI (Sigma). Images were acquired using an inverted epifluorescence microscope at × magnification of 40x.

Western blotting

Western blotting was performed as described previously³². Antibodies used for western blot analysis were from the following sources: β-actin (Cell Signaling, 8457, 1:2000), CDK8 (Cell Signaling, 4101, 1:1000), 4EBP1 (Cell Signaling, 9644S, 1:1000), phospho-4EBP1 (Cell Signaling, 2855S, 1:1000), STAT1 (Cell Signaling, 9176S, 1:1000), phospho-STAT1 (Cell Signaling, 8826S, 1:1000), S6 (Cell Signaling, 2217 T, 1:1000), phospho-S6 (Cell Signaling, 4858 T, 1:1000), RNA Pol II (Cell Signaling, 2629S, 1:1000), and phospho-RNA Pol II-Ser2 (Cell Signaling, 13499, 1:1000).

CDK8 and mTOR inhibitors

The CDK8 inhibitors RVU120, Torin1, and TAK-228 were purchased from MedChemExpress, and RVU120 for animal studies was provided by Ryvu Therapeutics. The drugs were reconstituted in dimethyl sulfoxide (DMSO). An equivalent amount of DMSO at the highest concentration of the drug was used for each experiment as a vehicle control.

Extreme limiting dilution assay

The cells were treated with the indicated concentrations of RVU120 and then seeded into 96-well ultra-low-attachment plates in neurosphere media at increasing concentrations from 1 to 250 cells/well. Cells were seeded from n = 5 wells (250 cells/well, 100 cells/well), n = 10 wells (10–50 cells/well), or n = 30 wells (1 cell/well) per condition. The cells were allowed to grow for 14 days, and the number of wells containing neurospheres was counted under a microscope.

Protein synthesis assay

The MB cells were plated at a density of 2000 cells/well in a 96-well plate and cultured overnight. The next day, the cells were treated with either vehicle or RVU120 for 1, 24, or 48 h. The cells were then collected and centrifuged at 400 × g and resuspended in OPP (O-propargyl-puromycin) working solution (Cayman Chemical, 601100). The mixed cells were incubated for 30 min at 37 °C for OPP labeling of translated peptides. Following incubation, cells were fixed, washed, and analyzed using flow cytometry.

Drug interaction assay

Medulloblastoma cells were plated in 96-well low-attachment plates and subjected to dose-response assessments for individual drugs, as well as various concentrations of drug combinations, with DMSO (0.1%) and media serving as controls. The growth inhibition was quantified using the CellTiter 96 AQueous Non-Radioactive Cell Proliferation Assay (Promega) and the Incucyte SX5 Live-Cell Analysis System (Sartorius). At least five independent trials were conducted to ensure reproducibility of the results. The Chou-Talalay median-effect model and the Bliss independence dose-response surface model were used to classify whether the two drugs interacted in an antagonistic, additive, or synergistic manner. For the Chou-Talalay median-effect model, CI > 1 indicated antagonism, CI = 1 demonstrated activity, and CI < 1 indicated synergistic interactions.

Unbound brain-to-plasma partition coefficient (Kpuu)

RVU120 was given to animals as a single dose of 10 mg/kg by an intravenous (rats, due to limited bioavailability in rats) and oral (mice) administration. At predefined time points (4 h for mice, 2 h for rats) animals were anesthetized and blood samples were collected by heart puncture using a heparinized syringe and centrifuged at 4 °C and 4000 × g for 5 min to obtain plasma. Immediately after the final blood sample was obtained, the lumbar CSF cwas ollected by a single lumbar puncture. Plasma CSF samples were stored at −20 °C until use.

The quantification of RVU120 in plasma and CSF samples was performed using liquid chromatography – tandem mass spectrometry method (LC/MS/MS). Briefly, the proteins in the samples (55 µL of plasma or 10 µL of CSF) were precipitated with 200 uL acetonitrile and, centrifugated at 4 °C and 10,000 g for 15 min and the supernatants were injected on LC/MS/MS. Compound was analyzed in multiple reaction monitoring (MRN) mode using a Sciex QTrap 5500 instrument (Torrance, MA, USA) equipped with Shimadzu DGU-20A5R(C) LC system (Kyoto, Japan) with Phenomenex Kinetex C18, 2.6 µ 100 A, 30*2.1 mm; (Torrance, CA, USA) as analytical chromatography column.

The unbound fraction of compound in murine and rat plasma was determined by equilibrium dialysis with Rapid Equilibrium Dialysis (RED) Device (Thermo Fisher Scientific, Rockford, IL, USA). Plasma samples were spiked with the test compound (1 or 5 µM) and were dialyzed versus buffer (150 mM sodium phosphate buffer). The 96-well equilibrium dialysis apparatus was maintained on a rotator (set at 100 rpm) in an incubator at 37 °C for 18 h. Samples, after unifying the matrix and protein precipitation, were vortexed and centrifuged for 20 minutes at 4°C at 2000 g. The supernatants were then transferred into the HPLC plate for LC-MS analysis.

CSF-to-plasma unbound concentration ratios (K_p,uu) were calculated as follows:

K_{p, u u} = \frac{C_{C S F}}{C_{p} \times f_{p}}

Where C_CSF, C_p, f_p represent respectively CSF concentration, plasma concentration and the unbound fraction in plasma.

RNA-seq

RNA was isolated from cells under the indicated experimental conditions using a Qiagen miRNAeasy kit (Valencia) and measured using an Agilent Bioanalyzer (Agilent Technologies). Illumina Novaseq 6000 libraries were prepared and sequenced by Novogene (CA, USA) or the Genomics and Microarray Core Facility at the University of Colorado Anschutz Medical Campus. High-quality base calls at Q30 ≥ 80% were obtained with approximately 40 M paired paired-end reads. Sequenced 150 bp pair-end reads were mapped to the human genome (GRCh38) by STAR (v2.4.0.1), read counts were calculated by R Bioconductor package GenomicAlignments (v1.18.1), and differential expression was analyzed with DESeq2 (v1.22.2) in R. Further analysis by GSEA was performed using GSEA (v2.1.0) software with 1000 data permutations and Cytoscape (v3.10.1).

Gene set enrichment analysis

Gene sets from MSigDB were downloaded and used to estimate biological activity. The ssGSEA algorithm in the R package GSVA (v.1.40.1) was applied to estimate signature enrichment in the bulk transcript datasets. The enrichment results of GO and pathways among differentially expressed genes were generated using the R package clusterProfiler (v.4.7.1).

CUT&RUN

A total of 500,000 cells per reaction were harvested and captured using 10 µL of pre-activated ConA beads (EpiCypher). Beads with attached cells were incubated at room temperature for 10 min to ensure complete adsorption. Subsequently, 50 µL of cold antibody specific to the reaction was added to each sample. The antibodies used for CUT&RUN were CDK8 (Cell signaling, 4101S, 1:50), MYC (Cell Signaling, 13987S, 1:50), RNA Pol II (Cell signaling, 2629S,1:50), phospho- RNA Pol II (Cell Signaling, 13499S, 1:50), H3K4me1 (Abcam, ab8895, 1:50), H3K4me3 (EpiCypher, 13-0041 K, 0.5 mg/ml), BRD4 (Cell signaling, 13440S, 1:50), H3K27ac (Active motif, 39133, 1:25), and IgG (EpiCypher, 13-0042 K, 0.5 mg/ml). The cells were then incubated overnight on a nutator at 4 °C and permeabilized using a buffer containing 5% digitonin. Next, 2.5 µL/reaction pAG-MNase (Epicypher) was added to each sample. The beads were gently resuspended by vortexing or pipetting to evenly distribute the enzymes. The mixture was incubated for 10 min at room temperature. Calcium Chloride (100 mM, 1 µL/reaction) was added to the reaction, followed by a 2-hour incubation at 4 °C. After incubation, 34 µL of Stop Master Mix was added to each tube, followed by a 10-minute incubation at 37 °C. The tubes were then quick-spun and placed on a magnet for slurry separation, and the clear supernatants were transferred to 8-strip tubes for DNA purification. Libraries were prepared using the NEBNext Ultra II DNA Library Prep kit and sequenced using NovaSeq PE150.

CUT&RUN-seq reads were aligned to the reference human genome hg38 using BOWTIE (v.2.3.4.1). Aligned reads were stripped of duplicate reads using Sambamba (v.0.6.8). Peaks were called using the program MACS (v2.1.2), with the narrow peak mode using matched input controls and a q-value of 0.00001. Peaks in the blacklisted genomic regions identified by the ENCODE consortium were excluded using bedtools. For downstream analysis and visualization, bamCoverage was used to generate bigwig files, and density maps were produced using IGV tools. Group 3 medulloblastoma enhancers were defined based on H3K27ac signals. Regions within 1 kb of RefSeq transcription start site (TSS) locations and peaks with strong H3K4me3 signals typical of active promoters were subtracted from these signals. Annotation and visualization of the peaks were conducted using ChIPseeker (v3.18). Differentially marked genes were calculated using DiffBind and DESeq2, based on the threshold of FDR < 0.05 and fold-change ≥ 2.

Single cell RNA-seq

Single-cell RNA sequencing data were aligned against a composite reference consisting of mm10 and hg38 genomes to delineate transcripts originating from murine and human cancer cells using the Cell Ranger toolkit (version 4.0.0). The classification of cells as either human or murine was based on a threshold of 90% genome-specific reads. Cells falling below this threshold were identified as human-mouse chimeric multiplets and excluded from further analysis. Gene-barcode count matrices obtained from scRNA-seq were processed using the Seurat package (version 4.0.3) in R. Cells with fewer than 500 or more than 8000 genes were excluded to eliminate low-quality samples and potential doublets. Cells with over 10% reads mapped to mitochondrial genes were filtered out. Log-normalization was applied to the filtered datasets, followed by principal component analysis to reduce the dimensionality. Utilizing Seurat’s elbow plot function, the top 25 principal components were selected for UMAP plot generation. Cell clusters were discerned via k-nearest neighbor unsupervised clustering, and the resolution parameter was set to 1.2. Established markers from literature were used to annotate each cluster with its corresponding biological cell type.

Multispectral IHC

Tumor tissues were fixed in formalin and paraffin-embedded for multispectral imaging using the Vectra 3.0 Automated Quantitative Pathology Imaging System (Perkin Elmer). Four-micron sections mounted on glass slides were sequentially stained for human CDK8 (Abcam, ab224828), MYC (Abcam, ab168727), RPS12 (Abcam, ab167428), p-4EBP1-T37/46 (Abcam, ab75831), p-S6-S235/236 (Cell Signaling, 2211S), p-AKT-S473 (Leica, NCL-L-AKT-PHOS), and DAPI using a Bond RX autostainer (Leica). Slides were dewaxed (Leica), heat-treated in ER2 (epitope retrieval solution 2) antigen retrieval buffer for 20 min at 93 °C (Leica), blocked in antibody (Ab) Diluent (Perkin Elmer), incubated for 30 min with the primary antibody, 10 min with horseradish peroxidase-conjugated secondary polymer (anti-mouse/anti-rabbit, Perkin Elmer), and 10 min with horseradish peroxidase-reactive OPAL fluorescent reagents (Perkin Elmer). Slides were washed between staining steps with Bond Wash (Leica) and stripped between each round of staining by heat treatment in antigen retrieval buffer. After the final round of staining, the slides were heat-treated in ER1 antigen retrieval buffer, stained with spectral 4′,6-diamidino-2-phenylindole (Perkin Elmer), and coverslipped with ProLong Diamond mounting media (Thermo Fisher). Whole slide scans were collected using a 10× objective lens at a resolution of 1.0 μm. Approximately 30 regions of interest were selected from the tumor in areas near the tumor border or in the center of the tumor. Regions of interest were scanned for multispectral imaging with a 20× objective lens at a resolution of 0.5 μm. Multispectral images were analyzed using inForm software version 2.3 (Perkin Elmer) to unmix adjacent fluorochromes, subtract autofluorescence, segment the tissue into tumor regions and stroma, segment the cells into nuclear, cytoplasmic, and membrane compartments, and phenotype the cells according to cell marker expression.

Animal studies

Animal care and experimental procedures were conducted in accordance with the guidelines of the University of Colorado Center for Comparative Medicine and the University of Colorado Institutional Animal Care and Use Committee (protocol number: 00052). Mice are maintained under humidity- and temperature-controlled conditions with a light/dark cycle that is set at 12 h. All mice will be housed in cages with micro-isolator lids. Animals are fed autoclaved mouse chow and water ad libitum. Handling is performed with universal sterile precautions, and experienced personnel will perform all procedures. If an animal loses >15% of its body weight or is observed to exhibit any signs of illness (dehydration, hunched appearance, hind-limb paralysis), it will be euthanized in accordance with IACUC protocols. Mice will be euthanized by CO₂ asphyxiation and decapitation. CO₂ asphyxiation, followed by secondary means of euthanasia, appears to cause the least amount of distress and pain and is consistent with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association. For mice requiring cardiac puncture for blood collection, they will undergo CO₂ asphyxiation first, and then a needle will be inserted under the left costal margin and aimed inward and upward until a flash is seen in the syringe; blood is then withdrawn and submitted for complete blood count.

Female athymic Nude Foxn1^nu and female NOD scid (NSG, #5557) gamma mice aged 4–8 weeks were used for orthotopic xenograft studies. D425 or D458 cells were collected and resuspended in a single cell suspension of 20,000 cells/3 μl in non-FBS medium. Tumor cells were injected into the cerebellum using the following coordinates for medulloblastoma implantation: 1.500 mm mediolateral (right), −2.000 mm anteroposterior (posterior) relative to lambda, and −3.000 mm dorsoventral (depth) from the skull surface at lambda. To monitor tumor growth in D458 xenograft mice, the mice were injected intraperitoneally with 10 μl/g of 15 mg/mL D-luciferin potassium salt solution (Gold Biotechnology) and imaged using the Xenogen IVIS 200 In vivo Imaging System (PerkinElmer). Tumor bioluminescence was analyzed using the Living Image 2.60.1 software (PerkinElmer). The mice used in this study were kept in a sterile environment under 12/12-h light/dark cycle, 21–23 °C and 40–60% humidity at University of Colorado, Anschutz Medial Campus, Aurora, USA. Since brain tumor burden cannot be reliably defined by size, mice were monitored daily for neurological function, body condition score, and weight loss. Animals were euthanized according to predefined humane endpoints in our approved protocol, including a maximum of 15% body weight loss.

The mice were administered a daily dose of 40 mg/kg RVU120 or 1 mg/kg TAK-228 via oral gavage. RVU120 was dissolved in water and TAK-228 was prepared by dilution in 0.5% N-methyl-2-pyrrolidone and subsequent suspension in a 0.5% polyvinylpyrrolidone solution for administration. In the combination treatment group, mice received RVU120 initially, followed by a 2-hour intermission before the administration of TAK-228. All mice were treated with the respective drugs 2-4 h before euthanasia, and blood was extracted for hematological toxicity analysis.

Immunohistochemistry

For histological analysis, tumors from experimental mice were dissected and either frozen or preserved in 10% formalin. The samples were rinsed with PBS, fixed in 4% paraformaldehyde overnight at 4 °C, and embedded in paraffin. Antigen retrieval was performed by the application of citrate buffer pH 6.00 for 20 min. Slides were then incubated with caspase 3 (Cell Signaling Technology, 9662) and H&E overnight at 4 °C. The secondary antibody conjugated to horseradish peroxidase was detected using the Dako EnVision Kit for 3,3′-diaminobenzidine.

Flow cytometry

Cells were fixed with 4% formaldehyde for 15 min at room temperature. The fixed cells were washed and permeabilized with methanol on ice for 10 min. Cells were stained with active caspase-3 (BD Biosciences). The apoptosis was measured by eBioscienc Annexin V apoptosis detection kit FITC (Thermo Fisher). Flow cytometry analysis was performed using an Amnis FlowSight flow cytometer (Millipore).

Microarray preparation and data processing

RNA from all surgical specimens was extracted, amplified, labeled, and hybridized to Affymetrix HG-U113 plus two microarray chips (Affymetrix). The scanned microarray data were background-corrected and normalized using the RMA algorithm, resulting in log 2 gene expression values. For the public microarray data, raw CEL files were downloaded from the Gene Expression Omnibus under accession number GSE85217 and normalized using the RMA algorithm. The gene expression array data generated using the Affymetrix Gene 1.1 ST array and U133 Plus 2.0, array platforms were merged to generate a combined value. For each platform, the contrast value per gene was calculated by subtracting the mean expression of that gene across all samples hybridized on that platform from each individual, and the resulting contrast values of the two platforms were then combined.

Magnetic resonance imaging

For in vivo MRI acquisitions, the mice were anesthetized shortly before and during the MR session using a 1.5% isoflurane/oxygen mixture. The anesthetized mice were placed on a temperature-controlled mouse bed below a mouse head array coil and inserted into a Bruker 9.4 Tesla BioSpec MR scanner (Bruker Medical). First, T2-weighted turboRARE images were acquired using the following parameters: repetition time (TR), 3268 ms; echo time (TE), 60 ms; RARE factor, 12 and 8 averages; FOV, 20 mm; matrix size, 350 × 350; slice thickness, 700 µm; 24 sagittal and axial slices; in-plane spatial resolution, 51 µm. A diffusion-weighted EPI sequence with 6 b values was then used using four axial slices covering all tumor lesions and unaffected brain tissue. Tumor regions were manually segmented on T2-weighted images by placing hand-drawn regions of interest (ROI) and the volume was calculated in mm3. The apparent diffusion coefficient (ADC; s/mm2) was calculated using diffusion-weighted imaging maps as a criterion for tumor cellularity. All acquisitions and image analyses were performed using the Bruker ParaVision NEO software (Bruker Medical).

CRISPR-Cas9 Screen

The CU Druggable Library (CUDL) consists of 9761 sgRNAs from 1140 genes targeted by 1194 FDA-approved drugs involved in metabolism, protein modifications, signal transduction, and kinases, and druggable genes of interest from multiple research labs in the University of Colorado’s Anschutz Medical Campus. The oligo pool for the sgRNA library was synthesized on a custom chip and cloned into lentiCRISPRv2 (#52961, Addgene) and lentiGUIDE-puro (#52963, Addgene). The library was transduced into D341, D425, and D458 cells in triplicate at a multiplicity of infection (MOI) of ~0.3. Uninfected cells were removed with puromycin selection, and the population was cultured for a total of 18 days, after which genomic DNA was harvested, the sgRNA cassette retrieved by PCR, and the abundance of sgRNAs quantitated by Illumina sequencing. The sequencing reads were aligned to the shRNA library using Bowtie and the ambiguous reads were excluded from the analysis. Resulting read counts per sgRNA were analyzed using R-based package MAGeCK (1.10.0), which ranks sgRNAs based on P-values calculated from the negative binomial model and uses a modified robust ranking aggregation (RRA) algorithm to identify positively or negatively selected sgRNA and genes.

Study approval

All patients provided written informed consent for molecular studies of their tumors, and the study protocol was approved by the ethics committee of the University of Colorado and Children’s Hospital Colorado (COMIRBs #95–500). All animal procedures were performed in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals and approved by the University of Colorado, Anschutz Campus Institutional Animal Care and Use Committee.

Statistics analysis

No formal statistical methods were used to predetermine sample sizes. Sample sizes were chosen based on established standards in the field and on our prior experience with similar experiments, and they are consistent with those used in related published studies. Samples/animals were randomly assigned to experimental groups to minimize allocation bias. Investigators were blinded to group allocation during both data collection and analysis. Statistical significance was set to p < 0.05. Transcriptomics data were analyzed using DESeq2 with an adjusted P value threshold of 0.05. R2: The Genomics Analysis and Visualization Platform (https://hgserver1.amc.nl/cgi-bin/r2/main.cgi?open_page=login) was used to delineate the association between gene expression levels and overall survival in patient samples. R package survival (v.3.2-11) and Prism GraphPad (v.10.0.2) were used for the statistics.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information^{(10.8MB, pdf)}

Reporting Summary^{(123.5KB, pdf)}

Transparent Peer Review file^{(400.9KB, pdf)}

Source data

Source Data^{(92.9MB, xlsx)}

Acknowledgements

The authors appreciate the contribution made by the Tissue Histology Shared Resource of University of Colorado, the Genomics Core of University of Colorado. We also acknowledge Dr. Craig Forester and Dr. Dylan Taatjes for review of the manuscript and valuable insight and discussion. National Institutes of Health grant P30CA046934 (University of Colorado Cancer Center), Morgan Adams Foundation (DW, RV), American Cancer Society to the University of Colorado Cancer Center (IRG #16-184-56, DW), Cancer League of Colorado, Inc (DW, RV), Alex’s Lemonade Stand Foundation (Young Investigator Grant, 1274784, DW), Kate Amato Foundation Research Grant (DW), Rally Foundation for Childhood Cancer Research (Young Investigator Grant, 25YIN42, DW) and American Brain Tumor Association Research (Discovery Grants, DG2500079, DW).

Author contributions

Conceptualization: D.W., R.V. Methodology: D.W., C.R., B.V., S.V., N.D., A.P., Y.L., A.S., B.B. Data analysis: D.W., E.D., Y.L., N.S. Blood-brain barrier analysis: K.K., M.M. Funding acquisition: D.W., R.V. Supervision: R.V. Writing – original draft: D.W., R.V., T.R., M.M. Writing – review & editing: C.F., D.T., D.W., R.V.

Peer review

Peer review information

Nature Communications thanks Liguo Zhang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

The previously published medulloblastoma microarray data used in this study are available in Gene Expression Omnibus (GEO) under accession code GSE85217⁴⁴. The previously published single-cell RNA-seq data used in this study are available in GEO under accession code GSE156053 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi)³⁹. The raw CUT&RUN and RNA-seq data generated in this study have all been deposited into GEO under the accession code GSE280973. Raw data supporting the findings of this study are available in the Source Data files, which include uncropped blot images, raw data corresponding to each figure. The remaining data are available within the Article, Supplementary Information or Source Data file. Source data are provided with this paper.

Competing interests

K.K., M.M. and T.R. are employees of RYVU Therapeutics. The remaining authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-025-64937-3.

References

1.Hovestadt, V. et al. Medulloblastomics revisited: biological and clinical insights from thousands of patients. Nat. Rev. Cancer20, 42–56 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Cho, Y. J. et al. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J. Clin. Oncol.29, 1424–1430 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol.29, 1408–1414 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Northcott, P. A. et al. Medulloblastoma. Nat. Rev. Dis. Prim.5, 11 (2019). [DOI] [PubMed] [Google Scholar]
5.Schwalbe, E. C. et al. Molecular and clinical heterogeneity within MYC-family amplified medulloblastoma is associated with survival outcomes: A multicenter cohort study. Neuro Oncol.27, 222–236 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Llombart, V. & Mansour, M. R. Therapeutic targeting of “undruggable” MYC. EBioMedicine75, 103756 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Mori, T. et al. c-Myc overexpression increases ribosome biogenesis and protein synthesis independent of mTORC1 activation in mouse skeletal muscle. Am. J. Physiol. Endocrinol. Metab.321, E551–E559 (2021). [DOI] [PubMed] [Google Scholar]
8.Morcelle, C. et al. Oncogenic MYC Induces the Impaired Ribosome Biogenesis Checkpoint and Stabilizes p53 Independent of Increased Ribosome Content. Cancer Res. 79, 4348–4359 (2019). [DOI] [PubMed] [Google Scholar]
9.Popay, T. M. et al. MYC regulates ribosome biogenesis and mitochondrial gene expression programs through its interaction with host cell factor-1. Elife10 (2021). [DOI] [PMC free article] [PubMed]
10.Sullivan, D. K. et al. MYC oncogene elicits tumorigenesis associated with embryonic, ribosomal biogenesis, and tissue-lineage dedifferentiation gene expression changes. Oncogene41, 4960–4970 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Jiao, L. et al. Ribosome biogenesis in disease: new players and therapeutic targets. Signal Transduct. Target Ther.8, 15 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Destefanis, F., Manara, V. & Bellosta, P. Myc as a Regulator of Ribosome Biogenesis and Cell Competition: A Link to Cancer. Int. J. Mol. Sci.21 (2020). [DOI] [PMC free article] [PubMed]
13.Grandori, C. et al. c-Myc binds to human ribosomal DNA and stimulates transcription of rRNA genes by RNA polymerase I. Nat. Cell Biol.7, 311–318 (2005). [DOI] [PubMed] [Google Scholar]
14.Chan, J. C. et al. AKT promotes rRNA synthesis and cooperates with c-MYC to stimulate ribosome biogenesis in cancer. Sci. Signal4, ra56 (2011). [DOI] [PubMed] [Google Scholar]
15.Campbell, K. J. & White, R. J. MYC regulation of cell growth through control of transcription by RNA polymerases I and III. Cold Spring Harb Perspect Med. 4 (2014). [DOI] [PMC free article] [PubMed]
16.Gomez-Roman, N. et al. Activation by c-Myc of transcription by RNA polymerases I, II and III. Biochem Soc Symp. 141–154 (2006). [DOI] [PubMed]
17.Oskarsson, T. & Trumpp, A. The Myc trilogy: lord of RNA polymerases. Nat. Cell Biol.7, 215–217 (2005). [DOI] [PubMed] [Google Scholar]
18.Staab, J., Herrmann-Lingen, C. & Meyer, T. CDK8 as the STAT1 serine 727 kinase?. JAKSTAT2, e24275 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Allen, B. L. & Taatjes, D. J. The Mediator complex: a central integrator of transcription. Nat. Rev. Mol. Cell Biol.16, 155–166 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Knab, V. M. et al. Triple-negative breast cancer cells rely on kinase-independent functions of CDK8 to evade NK-cell-mediated tumor surveillance. Cell Death Dis.12, 991 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Liang, J. et al. CDK8 Selectively Promotes the Growth of Colon Cancer Metastases in the Liver by Regulating Gene Expression of TIMP3 and Matrix Metalloproteinases. Cancer Res. 78, 6594–6606 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Han, S. I. & Lim, S. C. Expression and Prognostic Significance of CDK8 and beta-Catenin in Hepatocellular Carcinoma. Vivo34, 1387–1394 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Fukasawa, K. et al. CDK8 maintains stemness and tumorigenicity of glioma stem cells by regulating the c-MYC pathway. Oncogene40, 2803–2815 (2021). [DOI] [PubMed] [Google Scholar]
24.Hofmann, M. H. et al. Selective and Potent CDK8/19 Inhibitors Enhance NK-Cell Activity and Promote Tumor Surveillance. Mol. Cancer Ther.19, 1018–1030 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Rzymski, T. et al. SEL120-34A is a novel CDK8 inhibitor active in AML cells with high levels of serine phosphorylation of STAT1 and STAT5 transactivation domains. Oncotarget8, 33779–33795 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Yu, M. et al. Potent and orally bioavailable CDK8 inhibitors: Design, synthesis, structure-activity relationship analysis and biological evaluation. Eur. J. Med Chem.214, 113248 (2021). [DOI] [PubMed] [Google Scholar]
27.Lee, J. C., Liu, S., Wang, Y., Liang, Y. & Jablons, D. M. MK256 is a novel CDK8 inhibitor with potent antitumor activity in AML through downregulation of the STAT pathway. Oncotarget13, 1217–1236 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Ho, T. Y. et al. The study of a novel CDK8 inhibitor E966-0530-45418 that inhibits prostate cancer metastasis in vitro and in vivo. Biomed. Pharmacother.162, 114667 (2023). [DOI] [PubMed] [Google Scholar]
29.Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell95, 717–728 (1998). [DOI] [PubMed] [Google Scholar]
30.Stieg, D. C., Cooper, K. F. & Strich, R. The extent of cyclin C promoter occupancy directs changes in stress-dependent transcription. J. Biol. Chem.295, 16280–16291 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
31.van de Peppel, J. et al. Mediator expression profiling epistasis reveals a signal transduction pathway with antagonistic submodules and highly specific downstream targets. Mol. Cell19, 511–522 (2005). [DOI] [PubMed] [Google Scholar]
32.Wang, D. et al. A novel PLK1 inhibitor onvansertib effectively sensitizes MYC-driven medulloblastoma to radiotherapy. Neuro Oncol. (2021). [DOI] [PMC free article] [PubMed]
33.Veo, B. et al. Transcriptional control of DNA repair networks by CDK7 regulates sensitivity to radiation in MYC-driven medulloblastoma. Cell Rep.35, 109013 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature483, 603–607 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Boulay, G. et al. OTX2 Activity at Distal Regulatory Elements Shapes the Chromatin Landscape of Group 3 Medulloblastoma. Cancer Discov.7, 288–301 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Lu, Y. et al. OTX2 expression contributes to proliferation and progression in Myc-amplified medulloblastoma. Am. J. Cancer Res. 7, 647–656 (2017). [PMC free article] [PubMed] [Google Scholar]
37.Cheng, Y. et al. NeuroD1 Dictates Tumor Cell Differentiation in Medulloblastoma. Cell Rep.31, 107782 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Salsano, E. et al. Expression of the neurogenic basic helix-loop-helix transcription factor NEUROG1 identifies a subgroup of medulloblastomas not expressing ATOH1. Neuro Oncol.9, 298–307 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Riemondy, K. A. et al. Neoplastic and immune single-cell transcriptomics define subgroup-specific intra-tumoral heterogeneity of childhood medulloblastoma. Neuro Oncol.24, 273–286 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Bancerek, J. et al. CDK8 kinase phosphorylates transcription factor STAT1 to selectively regulate the interferon response. Immunity38, 250–262 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Loryan, I. et al. Unbound Brain-to-Plasma Partition Coefficient, K(p,uu,brain)-a Game Changing Parameter for CNS Drug Discovery and Development. Pharm. Res. 39, 1321–1341 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Liu, J., Xu, Y., Stoleru, D. & Salic, A. Imaging protein synthesis in cells and tissues with an alkyne analog of puromycin. Proc. Natl Acad. Sci. USA109, 413–418 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Wang, D. et al. A Regulatory Loop of FBXW7-MYC-PLK1 Controls Tumorigenesis of MYC-Driven Medulloblastoma. Cancers (Basel)13 (2021). [DOI] [PMC free article] [PubMed]
44.Cavalli, F. M. G. et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. Cancer Cell31, 737–754.e736 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Fan, Q. et al. A Kinase Inhibitor Targeted to mTORC1 Drives Regression in Glioblastoma. Cancer Cell31, 424–435 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Wu, C. C. et al. mTORC1-Mediated Inhibition of 4EBP1 Is Essential for Hedgehog Signaling-Driven Translation and Medulloblastoma. Dev. Cell43, 673–688.e675 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Chaturvedi, N. K. et al. A Novel Combination Approach Targeting an Enhanced Protein Synthesis Pathway in MYC-driven (Group 3) Medulloblastoma. Mol. Cancer Ther.19, 1351–1362 (2020). [DOI] [PubMed] [Google Scholar]
48.Pei, Y. et al. HDAC and PI3K antagonists cooperate to inhibit growth of myc-driven medulloblastoma. Cancer Cell29, 311–323 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Maynard, R. E. et al. TORC1/2 kinase inhibition depletes glutathione and synergizes with carboplatin to suppress the growth of MYC-driven medulloblastoma. Cancer Lett.504, 137–145 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature488, 49–56 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Gajjar, A. et al. Outcomes by clinical and molecular features in children with medulloblastoma treated with risk-adapted therapy: results of an international phase III Trial (SJMB03). J. Clin. Oncol.39, 822–835 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Kumar, R. et al. Clinical Outcomes and Patient-Matched Molecular Composition of Relapsed Medulloblastoma. J. Clin. Oncol., JCO2001359 (2021). [DOI] [PMC free article] [PubMed]
53.Andrysik, Z., Bender, H., Galbraith, M. D. & Espinosa, J. M. Multi-omics analysis reveals contextual tumor suppressive and oncogenic gene modules within the acute hypoxic response. Nat. Commun.12, 1375 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Sooraj, D. et al. MED12 and BRD4 cooperate to sustain cancer growth upon loss of mediator kinase. Mol. Cell82, 123–139.e127 (2022). [DOI] [PubMed] [Google Scholar]
55.Nagel, R., Semenova, E. A. & Berns, A. Drugging the addict: non-oncogene addiction as a target for cancer therapy. EMBO Rep.17, 1516–1531 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Solimini, N. L., Luo, J. & Elledge, S. J. Non-oncogene addiction and the stress phenotype of cancer cells. Cell130, 986–988 (2007). [DOI] [PubMed] [Google Scholar]
57.Lee, L. J. et al. Cancer Plasticity: The Role of mRNA Translation. Trends Cancer7, 134–145 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Gilles, A. et al. Targeting the Human 80S Ribosome in Cancer: From Structure to Function and Drug Design for Innovative Adjuvant Therapeutic Strategies. Cells9 (2020). [DOI] [PMC free article] [PubMed]
59.Gebauer, F. & Hentze, M. W. Molecular mechanisms of translational control. Nat. Rev. Mol. Cell Biol.5, 827–835 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Ma, X. M. & Blenis, J. Molecular mechanisms of mTOR-mediated translational control. Nat. Rev. Mol. Cell Biol.10, 307–318 (2009). [DOI] [PubMed] [Google Scholar]
61.Poortinga, G. et al. c-MYC coordinately regulates ribosomal gene chromatin remodeling and Pol I availability during granulocyte differentiation. Nucleic Acids Res. 39, 3267–3281 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Coronel-Hernandez, J. et al. Aberrant metabolism as inductor of epigenetic changes in breast cancer: therapeutic opportunities. Front Oncol.11, 676562 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Robinson, G. et al. Novel mutations target distinct subgroups of medulloblastoma. Nature488, 43–48 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Lourenco, C. et al. MYC protein interactors in gene transcription and cancer. Nat. Rev. Cancer21, 579–591 (2021). [DOI] [PubMed] [Google Scholar]
65.Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell21, 155–167 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Wang, C. et al. Alternative approaches to target Myc for cancer treatment. Signal Transduct. Target Ther.6, 117 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature511, 428–434 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Tao, R. et al. MYC Drives Group 3 Medulloblastoma through Transformation of Sox2(+) Astrocyte Progenitor Cells. Cancer Res. 79, 1967–1980 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Information^{(10.8MB, pdf)}

Reporting Summary^{(123.5KB, pdf)}

Transparent Peer Review file^{(400.9KB, pdf)}

Source Data^{(92.9MB, xlsx)}

Data Availability Statement

[CR1] 1.Hovestadt, V. et al. Medulloblastomics revisited: biological and clinical insights from thousands of patients. Nat. Rev. Cancer20, 42–56 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR2] 2.Cho, Y. J. et al. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J. Clin. Oncol.29, 1424–1430 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol.29, 1408–1414 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR4] 4.Northcott, P. A. et al. Medulloblastoma. Nat. Rev. Dis. Prim.5, 11 (2019). [DOI] [PubMed] [Google Scholar]

[CR5] 5.Schwalbe, E. C. et al. Molecular and clinical heterogeneity within MYC-family amplified medulloblastoma is associated with survival outcomes: A multicenter cohort study. Neuro Oncol.27, 222–236 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR6] 6.Llombart, V. & Mansour, M. R. Therapeutic targeting of “undruggable” MYC. EBioMedicine75, 103756 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Mori, T. et al. c-Myc overexpression increases ribosome biogenesis and protein synthesis independent of mTORC1 activation in mouse skeletal muscle. Am. J. Physiol. Endocrinol. Metab.321, E551–E559 (2021). [DOI] [PubMed] [Google Scholar]

[CR8] 8.Morcelle, C. et al. Oncogenic MYC Induces the Impaired Ribosome Biogenesis Checkpoint and Stabilizes p53 Independent of Increased Ribosome Content. Cancer Res. 79, 4348–4359 (2019). [DOI] [PubMed] [Google Scholar]

[CR9] 9.Popay, T. M. et al. MYC regulates ribosome biogenesis and mitochondrial gene expression programs through its interaction with host cell factor-1. Elife10 (2021). [DOI] [PMC free article] [PubMed]

[CR10] 10.Sullivan, D. K. et al. MYC oncogene elicits tumorigenesis associated with embryonic, ribosomal biogenesis, and tissue-lineage dedifferentiation gene expression changes. Oncogene41, 4960–4970 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Jiao, L. et al. Ribosome biogenesis in disease: new players and therapeutic targets. Signal Transduct. Target Ther.8, 15 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR12] 12.Destefanis, F., Manara, V. & Bellosta, P. Myc as a Regulator of Ribosome Biogenesis and Cell Competition: A Link to Cancer. Int. J. Mol. Sci.21 (2020). [DOI] [PMC free article] [PubMed]

[CR13] 13.Grandori, C. et al. c-Myc binds to human ribosomal DNA and stimulates transcription of rRNA genes by RNA polymerase I. Nat. Cell Biol.7, 311–318 (2005). [DOI] [PubMed] [Google Scholar]

[CR14] 14.Chan, J. C. et al. AKT promotes rRNA synthesis and cooperates with c-MYC to stimulate ribosome biogenesis in cancer. Sci. Signal4, ra56 (2011). [DOI] [PubMed] [Google Scholar]

[CR15] 15.Campbell, K. J. & White, R. J. MYC regulation of cell growth through control of transcription by RNA polymerases I and III. Cold Spring Harb Perspect Med. 4 (2014). [DOI] [PMC free article] [PubMed]

[CR16] 16.Gomez-Roman, N. et al. Activation by c-Myc of transcription by RNA polymerases I, II and III. Biochem Soc Symp. 141–154 (2006). [DOI] [PubMed]

[CR17] 17.Oskarsson, T. & Trumpp, A. The Myc trilogy: lord of RNA polymerases. Nat. Cell Biol.7, 215–217 (2005). [DOI] [PubMed] [Google Scholar]

[CR18] 18.Staab, J., Herrmann-Lingen, C. & Meyer, T. CDK8 as the STAT1 serine 727 kinase?. JAKSTAT2, e24275 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR19] 19.Allen, B. L. & Taatjes, D. J. The Mediator complex: a central integrator of transcription. Nat. Rev. Mol. Cell Biol.16, 155–166 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Knab, V. M. et al. Triple-negative breast cancer cells rely on kinase-independent functions of CDK8 to evade NK-cell-mediated tumor surveillance. Cell Death Dis.12, 991 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Liang, J. et al. CDK8 Selectively Promotes the Growth of Colon Cancer Metastases in the Liver by Regulating Gene Expression of TIMP3 and Matrix Metalloproteinases. Cancer Res. 78, 6594–6606 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR22] 22.Han, S. I. & Lim, S. C. Expression and Prognostic Significance of CDK8 and beta-Catenin in Hepatocellular Carcinoma. Vivo34, 1387–1394 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Fukasawa, K. et al. CDK8 maintains stemness and tumorigenicity of glioma stem cells by regulating the c-MYC pathway. Oncogene40, 2803–2815 (2021). [DOI] [PubMed] [Google Scholar]

[CR24] 24.Hofmann, M. H. et al. Selective and Potent CDK8/19 Inhibitors Enhance NK-Cell Activity and Promote Tumor Surveillance. Mol. Cancer Ther.19, 1018–1030 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Rzymski, T. et al. SEL120-34A is a novel CDK8 inhibitor active in AML cells with high levels of serine phosphorylation of STAT1 and STAT5 transactivation domains. Oncotarget8, 33779–33795 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Yu, M. et al. Potent and orally bioavailable CDK8 inhibitors: Design, synthesis, structure-activity relationship analysis and biological evaluation. Eur. J. Med Chem.214, 113248 (2021). [DOI] [PubMed] [Google Scholar]

[CR27] 27.Lee, J. C., Liu, S., Wang, Y., Liang, Y. & Jablons, D. M. MK256 is a novel CDK8 inhibitor with potent antitumor activity in AML through downregulation of the STAT pathway. Oncotarget13, 1217–1236 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR28] 28.Ho, T. Y. et al. The study of a novel CDK8 inhibitor E966-0530-45418 that inhibits prostate cancer metastasis in vitro and in vivo. Biomed. Pharmacother.162, 114667 (2023). [DOI] [PubMed] [Google Scholar]

[CR29] 29.Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell95, 717–728 (1998). [DOI] [PubMed] [Google Scholar]

[CR30] 30.Stieg, D. C., Cooper, K. F. & Strich, R. The extent of cyclin C promoter occupancy directs changes in stress-dependent transcription. J. Biol. Chem.295, 16280–16291 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.van de Peppel, J. et al. Mediator expression profiling epistasis reveals a signal transduction pathway with antagonistic submodules and highly specific downstream targets. Mol. Cell19, 511–522 (2005). [DOI] [PubMed] [Google Scholar]

[CR32] 32.Wang, D. et al. A novel PLK1 inhibitor onvansertib effectively sensitizes MYC-driven medulloblastoma to radiotherapy. Neuro Oncol. (2021). [DOI] [PMC free article] [PubMed]

[CR33] 33.Veo, B. et al. Transcriptional control of DNA repair networks by CDK7 regulates sensitivity to radiation in MYC-driven medulloblastoma. Cell Rep.35, 109013 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature483, 603–607 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Boulay, G. et al. OTX2 Activity at Distal Regulatory Elements Shapes the Chromatin Landscape of Group 3 Medulloblastoma. Cancer Discov.7, 288–301 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Lu, Y. et al. OTX2 expression contributes to proliferation and progression in Myc-amplified medulloblastoma. Am. J. Cancer Res. 7, 647–656 (2017). [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Cheng, Y. et al. NeuroD1 Dictates Tumor Cell Differentiation in Medulloblastoma. Cell Rep.31, 107782 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR38] 38.Salsano, E. et al. Expression of the neurogenic basic helix-loop-helix transcription factor NEUROG1 identifies a subgroup of medulloblastomas not expressing ATOH1. Neuro Oncol.9, 298–307 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR39] 39.Riemondy, K. A. et al. Neoplastic and immune single-cell transcriptomics define subgroup-specific intra-tumoral heterogeneity of childhood medulloblastoma. Neuro Oncol.24, 273–286 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR40] 40.Bancerek, J. et al. CDK8 kinase phosphorylates transcription factor STAT1 to selectively regulate the interferon response. Immunity38, 250–262 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.Loryan, I. et al. Unbound Brain-to-Plasma Partition Coefficient, K(p,uu,brain)-a Game Changing Parameter for CNS Drug Discovery and Development. Pharm. Res. 39, 1321–1341 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR42] 42.Liu, J., Xu, Y., Stoleru, D. & Salic, A. Imaging protein synthesis in cells and tissues with an alkyne analog of puromycin. Proc. Natl Acad. Sci. USA109, 413–418 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Wang, D. et al. A Regulatory Loop of FBXW7-MYC-PLK1 Controls Tumorigenesis of MYC-Driven Medulloblastoma. Cancers (Basel)13 (2021). [DOI] [PMC free article] [PubMed]

[CR44] 44.Cavalli, F. M. G. et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. Cancer Cell31, 737–754.e736 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR45] 45.Fan, Q. et al. A Kinase Inhibitor Targeted to mTORC1 Drives Regression in Glioblastoma. Cancer Cell31, 424–435 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR46] 46.Wu, C. C. et al. mTORC1-Mediated Inhibition of 4EBP1 Is Essential for Hedgehog Signaling-Driven Translation and Medulloblastoma. Dev. Cell43, 673–688.e675 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Chaturvedi, N. K. et al. A Novel Combination Approach Targeting an Enhanced Protein Synthesis Pathway in MYC-driven (Group 3) Medulloblastoma. Mol. Cancer Ther.19, 1351–1362 (2020). [DOI] [PubMed] [Google Scholar]

[CR48] 48.Pei, Y. et al. HDAC and PI3K antagonists cooperate to inhibit growth of myc-driven medulloblastoma. Cancer Cell29, 311–323 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR49] 49.Maynard, R. E. et al. TORC1/2 kinase inhibition depletes glutathione and synergizes with carboplatin to suppress the growth of MYC-driven medulloblastoma. Cancer Lett.504, 137–145 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR50] 50.Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature488, 49–56 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR51] 51.Gajjar, A. et al. Outcomes by clinical and molecular features in children with medulloblastoma treated with risk-adapted therapy: results of an international phase III Trial (SJMB03). J. Clin. Oncol.39, 822–835 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR52] 52.Kumar, R. et al. Clinical Outcomes and Patient-Matched Molecular Composition of Relapsed Medulloblastoma. J. Clin. Oncol., JCO2001359 (2021). [DOI] [PMC free article] [PubMed]

[CR53] 53.Andrysik, Z., Bender, H., Galbraith, M. D. & Espinosa, J. M. Multi-omics analysis reveals contextual tumor suppressive and oncogenic gene modules within the acute hypoxic response. Nat. Commun.12, 1375 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Sooraj, D. et al. MED12 and BRD4 cooperate to sustain cancer growth upon loss of mediator kinase. Mol. Cell82, 123–139.e127 (2022). [DOI] [PubMed] [Google Scholar]

[CR55] 55.Nagel, R., Semenova, E. A. & Berns, A. Drugging the addict: non-oncogene addiction as a target for cancer therapy. EMBO Rep.17, 1516–1531 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR56] 56.Solimini, N. L., Luo, J. & Elledge, S. J. Non-oncogene addiction and the stress phenotype of cancer cells. Cell130, 986–988 (2007). [DOI] [PubMed] [Google Scholar]

[CR57] 57.Lee, L. J. et al. Cancer Plasticity: The Role of mRNA Translation. Trends Cancer7, 134–145 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR58] 58.Gilles, A. et al. Targeting the Human 80S Ribosome in Cancer: From Structure to Function and Drug Design for Innovative Adjuvant Therapeutic Strategies. Cells9 (2020). [DOI] [PMC free article] [PubMed]

[CR59] 59.Gebauer, F. & Hentze, M. W. Molecular mechanisms of translational control. Nat. Rev. Mol. Cell Biol.5, 827–835 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR60] 60.Ma, X. M. & Blenis, J. Molecular mechanisms of mTOR-mediated translational control. Nat. Rev. Mol. Cell Biol.10, 307–318 (2009). [DOI] [PubMed] [Google Scholar]

[CR61] 61.Poortinga, G. et al. c-MYC coordinately regulates ribosomal gene chromatin remodeling and Pol I availability during granulocyte differentiation. Nucleic Acids Res. 39, 3267–3281 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR62] 62.Coronel-Hernandez, J. et al. Aberrant metabolism as inductor of epigenetic changes in breast cancer: therapeutic opportunities. Front Oncol.11, 676562 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR63] 63.Robinson, G. et al. Novel mutations target distinct subgroups of medulloblastoma. Nature488, 43–48 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR64] 64.Lourenco, C. et al. MYC protein interactors in gene transcription and cancer. Nat. Rev. Cancer21, 579–591 (2021). [DOI] [PubMed] [Google Scholar]

[CR65] 65.Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell21, 155–167 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR66] 66.Wang, C. et al. Alternative approaches to target Myc for cancer treatment. Signal Transduct. Target Ther.6, 117 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR67] 67.Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature511, 428–434 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR68] 68.Tao, R. et al. MYC Drives Group 3 Medulloblastoma through Transformation of Sox2(+) Astrocyte Progenitor Cells. Cancer Res. 79, 1967–1980 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Transcriptional regulation of protein synthesis by mediator kinase represents a therapeutic vulnerability in MYC-driven medulloblastoma

Dong Wang

Caitlin Ritz

Yuhuan Luo

Ammu Suresh

Angela Pierce

Bethany Veo

Breauna Brunt

Nathan Dahl

Natalie Serkova

Sujatha Venkataraman

Etienne Danis

Kamil Kus

Milena Mazan

Tomasz Rzymski

Rajeev Vibhakar

Abstract

Introduction

Results

CDK8 is a specific vulnerability in MYC-driven medulloblastoma

Fig. 1. CDK8 is a specific vulnerability in MYC-driven medulloblastoma.

Targeting medulloblastoma with CDK8 inhibitor

Fig. 2. Targeting medulloblastoma with CDK8 inhibitor.

CDK8 sustains MYC transcriptional activity in medulloblastoma

Fig. 3. CDK8 sustains MYC transcriptional activity in medulloblastoma.

CDK8 regulates protein synthesis in MYC-driven medulloblastoma

Fig. 4. CDK8 regulates protein synthesis in MYC-driven medulloblastoma.

Fig. 5. Interplay between MYC and CDK8 in controlling protein synthesis.

Interplay between MYC and CDK8 in controlling protein synthesis

CDK8 transcriptionally regulates the expression of ribosomal genes

Fig. 6. Chromatin binding profiles of CDK8 in MB cells.

Fig. 7. CDK8 transcriptionally regulates the expression of ribosomal genes.

CDK8 regulates mTOR signaling in MYC-driven medulloblastoma

Fig. 8. CDK8 regulates mTOR signaling in MYC-driven medulloblastoma.

Synergistic targeting of CDK8 and mTOR in MYC-Driven medulloblastoma

Fig. 9. Synergistic targeting of CDK8 and mTOR in MYC-Driven medulloblastoma.

Discussion

Methods

Brain samples collection

Cell lines

Transfection

Methylcellulose assay

Aldehyde dehydrogenase assay

Neurosphere assay

Immunofluorescence

Western blotting

CDK8 and mTOR inhibitors

Extreme limiting dilution assay

Protein synthesis assay

Drug interaction assay

Unbound brain-to-plasma partition coefficient (Kpuu)

RNA-seq

Gene set enrichment analysis

CUT&RUN

Single cell RNA-seq

Multispectral IHC

Animal studies

Immunohistochemistry

Flow cytometry

Microarray preparation and data processing

Magnetic resonance imaging

CRISPR-Cas9 Screen

Study approval

Statistics analysis

Reporting summary

Supplementary information

Source data

Acknowledgements

Author contributions

Peer review

Peer review information

Data availability

Competing interests

Footnotes

Supplementary information

References

Associated Data

Supplementary Materials

Data Availability Statement