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

Domain: neuroscience
Published: 2025-12-16
Editorial outcome: major_revision

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.

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 42,3. 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 synthesis7–10. 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 cytoplasm7,9,12–14. Chromatin remodeling is an essential aspect of these processes through which MYC directly activates RNA polymerases15–17. 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 II18,19. 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 target20–23. Several studies have determined the efficacy of CDK8 inhibitors in preclinical cancer models24–28. 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 stimuli29–31.

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.

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 development25–28. 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).

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 3 β and 3 γ 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).

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.

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 1µM 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 expression32,33,43. 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 4μM RVU120, while ONS76 cells showed no response, contrasting with the suppression observed at 0.5–1μM 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 4μM (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). 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 increase 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).

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 inhibitors45–48. 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).

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 Torin1 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).

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.

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, 1mM 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, 1μg/ml heparin, 2mM L-glutamine, 1% penicillin/streptomycin, 25ng/ml fibroblast growth factor (FGF), and 25ng/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 previously33,43.

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, 2217T, 1:1000), phospho-S6 (Cell Signaling, 4858T, 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 Cell Titer 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 10mg/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 c was 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 10,000g 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 100A, 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 2000g. The supernatants were then transferred into the HPLC plate for LC-MS analysis.
CSF-to-plasma unbound concentration ratios (Kp,uu) were calculated as follows:
Kp,uu = C_CSF / (C_p * f_p) (1)
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 miRNeasy 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 40M paired paired-end reads. Sequenced 150bp 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, 13-0041K, 0.5 mg/ml), BRD4 (Cell signaling, 13440S, 1:50), H3K27ac (Active motif, 39133, 1:25), and IgG (EpiCypher, 13-0042K, 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 IGVtools. Group 3 medulloblastoma enhancers were defined based on H3K27ac signals. Regions within 1kb 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 Foxn1nu 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 log2 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.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Wang and coworkers are presenting an interesting manuscript that describes the importance of the kinase CDK8 and targeting strategies against CDK8 in pediatric cancer. A certain focus is dedicated towards MYC-driven medulloblastoma (MB), a very aggressive pediatric brain tumor with poor prognosis. The manuscript is well written and the presented experimental data is comprehensive, reasonable and rational. The authors have used several methods and preclinical techniques providing compelling evidence for the role of CDK8 in MYC-driven Group 3 MB maintenance and proof-of-concept data for a promising CDK8 inhibitor that can be used as a potential treatment for this MB subgroup. Still, a few questions and concerns remain and can be summarized here below:\nMajor points:\n1. Authors state that MB subgroups 3 beta and 3 gamma have higher CDK8 levels, likely because of high MYC levels. It is shown in Fig. 1g that CDK8 expression correlate with poor survival, but it is also evident that some SHH tumors present with high CDK8 levels. Given that SHH MBs have high levels of MYCN (that shows a mutually exclusive expression pattern as compared to MYC), it would be interesting to see if CDK8 correlates with MYCN as well. For example, Chipumuro et al. Cell, 2014 suggests that shCDK8 reduces MYCN levels and correlating gene expression of CDK8 with MYC and/or MYCN (in bulk or single RNA-Seq data) would be useful and easy to include. Further, CDK8 and CDK19 would likely anti-correlate in specific subgroups of patients. Such data is however never shown.\n2. If CDK8 is low in normal cells CDK8 could be regulated by increased MYC levels in these tumors? If CDK8 itself is not amplified in patients it must be epigenetically activated or turned on by MYC. To show if MYC regulates CDK8 levels knocking MYC out in cells would lead to decreased levels of CDK8. This would be an important experiment that would improve the understanding of the CDK8-MYC relationship in this story.\n3. RVU120 data show promising results in cell killing of Group 3 MB cell lines. Most lines have MYC amplification or overexpression. In Fig 2b-e, apart from NHAs including a MYC low MB cell line would have been useful in order to understand the specificity of RVU120 on MYC-driven cancers. Again, it is not clear if RVU120 would inhibit MYC levels in these lines.\n4. I am happy to see that more clinically relevant PDX models are included and presented in Fig. 2k. However, why are not overall survival (OS) shown in these models after RVU120 treatment? If survival is not significant this should be stated.\n5. There is no discussion of toxicity and/or side effects of knockdown, depletion or inhibition of CDK8, which should also be very essential. For RUV120, hematology toxicity is tested which is good. Still, using RVU120 in combination with radiation treatment or standard chemotherapy would also be both useful and convincing for future upcoming trials.\n6. The CDK8 kinase is a part of the Mediator complex, which regulates gene transcription by RNA polymerase II. Thus, it is not surprising that ribosome biogenesis genes are regulated when manipulating CDK8 levels. In Fig 6. RVU120 shows an effect of p-Pol2. As an important comparison in several panels, other RNA Pol inhibitors (α-amanitin or actinomycin D) could have be tested as a positive control.\n7. That mTOR inhibition is a useful targeting strategy for these tumors is well known and expected. The data on synergy of mTOR inhibition and RVU120 is convincing. Still, RVU120 in combination with chemotherapies (cisplatin or cyclophosphamide) or radiation therapy is never presented. Such data would be important to show, as if they are showing good response/synergy it might be worth combining CDK8 inhibitors with standard of care.\nMinor points\n1. In Fig. 1e (and Fig. 4b) single cell data is used from MB patients. It is unclear in text and methods if this is already published data or newly generated data.\n2. Is the cohort described in Fig. 1f from Cavalli et al.? If so, this set is not properly cited.\n3. When describing how CDK8 regulate MYC levels in Fig 1i, it would be important to refer to the Fukusawa et al. paper that already showed this relationship in other brain tumors.\n4. Labeling and arrangement of panels are not optimal for Figure 1. In general, some Figure legends are not always as informative as main text, and should be improved.\n5. In Fig. 2e. to the left, the concentration of RVU120 is not indicated. In Fig 2e. to the right: no time point indicated when the lysate was collected. That there was changes in pSTAT1/STAT1 ratio in D425, but not in D458 is not discussed.\n6. Related to above, number of cell lines are listed in the method section without any data, and selection of D425 and D458 as representative cell lines is not motivated; the difference in results and potential underlying genetic causes in D425 and D458 results are not discussed.\n7. In Fig. 2j “SEL120” instead of RUV120 is written.\n8. In Fig. 6, the results are inconsistent. Thus, I do not get to conclude that “RVU120 is more efficient” if the result is not the same? Please clarify and modify your conclusion related to the data shown in:\nFig. 6a. decreased ppol2 and pol2 (RVU120)\nFig. 6b. increased pol2 and decreased ppol2 (shCDK2)\nFig. 6g. decreased ppol2 and pol2 (RVU120)\nFig. 6d. increased cytosolic and mitochondrial pol2\nFig. 6i. decreased cytosolic mitochondrial pol2 (RVU120)\n9. For many in vivo studies, information about the administration of drug(s) is missing, e.g. duration of administration and stop points.\nFor example, in Fig. 7 and Fig. 8: RVU120 and TAK-228 are given by daily oral gavage. But for how long was it administrated? Does the tumor start to grow and mice start to get sick during treatment?\n10. In Methods under Compounds, please change to “CDK8 and mTOR inhibitors”.\n11. Please present more information on the lentivirus transduction protocol used and specifics of shRNA targeting in the method section?", "reviewer_2": "The manuscript presented by Wang et al. identifies CDK8 as a critical therapeutic target in MYC-driven medulloblastoma (MB), a highly aggressive brain cancer. It proposes that CDK8 regulates MYC expression, ribosome biogenesis, and protein synthesis, driving MB tumor growth. Using inhibitors like RVU120 and combining CDK8 and mTOR inhibition show potential for enhanced therapeutic efficacy in preclinical models. Although the data included in the manuscript is convincing and interesting, further experimental evidence are required to support their conclusions. I strongly believe that is important to provide much stronger evidence to determine whether CDK8 regulates protein synthesis directly or indirectly through MYC. Additionally, the authors should better discuss this aspect, considering that MYC is a direct regulator of ribosome biogenesis at multiple levels (this review provides references to excellent articles on the topic: https://doi.org/10.1038/nrc2819). Moreover, while the RNA-seq data are very robust, it would be beneficial to include an additional experiment to demonstrate the impact on rRNA transcription or its processing. In addition, throughout the manuscript, the authors should try to justify why they use shRNA, sgRNA or siRNA against CDK8 along the manuscript.\nMajor points:\n1. In Figure 1, the authors demonstrates that CDK8 is a critical therapeutic vulnerability in MYC-driven medulloblastoma (MB). Through CRISPR-Cas9 screening, CDK8 was identified as essential for tumor growth, with its loss reducing MYC expression, cell proliferation, and neurosphere growth. High CDK8 expression correlates with poor survival in high-MYC MB, and its inhibition in mice significantly reduced tumor growth and extended survival, emphasizing its potential as a therapeutic target.\n-Why in Figure 1i the authors did not validate the results by making CDK8 knock outs?. In the same figure the authors mention a “notable decrease in MYC levels” (line 2; page5), however, there doesn’t seem to be much difference between the two shCDK8 constructs, despite the differences in CDK8 depletion efficiency, especially in D425 cell line. Quantifying these levels could provide greater clarity. Validation would be more robust using one sgRNA from the CRISPR screening library and a different one as well.\n-Importantly, there is no information on how the CRISPR screening was performed, which library was used, how many sgRNAs were used per gene, or how many of the CDK8 guides indicated vulnerability in MYC-driven MB tumors. This information is essential in order to publish the paper.\n2. Base on the results shown in Figure 3, the authors claim that CDK8 depletion leads to significant downregulation of genes associated with mRNA translation, ribosome assembly, and protein synthesis. As far as I know, this is the first time a direct regulation of translation by CDK8 has been suggested. Important controls should be included:\n- Polysome profiling in cells treated with the inhibitor (as is sown in Fig. 4g, are required). Quantifying polysomes will offer a more comprehensive and robust analysis of the impact of CDK8 depletion on the distribution between polysomes, monosomes, and individual ribosomal subunits (40S and 60S).\n- Is rRNA transcription affected? EU incorporation assays would help clarify this point. The text suggests that CDK8 depletion might impact rRNA maturation, but this is not demonstrated. Specific PCR assays or northern blot should be conducted to study rRNA processing and provide evidence for this hypothesis.\n- The authors suggest that CDK8 depletion might impact protein synthesis independently of MYC; however, the results are not entirely convincing, and additional experiments should be included in the manuscript. For this, I suggest to the authors to study the effect of a complete MYC depletion in cells treated with the CDK8 inhibitor or in CDK8 knockout cells, and analyze the impact in OPP, EU, rRNA processing, and survival curves.\n3. Regarding Figure 4g, the authors state that the observed effect on the 80S peak \"indicates that CDK8 mediates changes in ribosomal activity in MYC31 MB cells and is essential for continued ribosomal subunit assembly and overall protein synthesis.\" However, I do not consider this difference sufficient to support such a claim. How do the authors demonstrate a problem in ribosomal assembly? I do not see evidence of half-mers or a loss of the 60S peak relative to the 40S. Another aspect of this figure that could be helpful is performing an OPP assay on CDK8-KO cells.\n- Regarding Figure 4h, why do the authors only discuss changes in 5.8S rRNA? As far as I know, this dye labels all rRNA. To support this point, a Northern blot or PCR to quantify the abundance of different rRNA fragments would be necessary.\n4. In Figure 7d, the authors mention changes in p-4EBP1 but not in p-S6; however, this effect is not discussed in the text. Could the decrease in p-4EBP1 levels be independent of mTOR?. Later, with the inhibitor, both p-4EBP1 and p-S6 are affected at longer time points. What explanation do the authors provide for this observation? Additionally, what is the viability of the cells at these time points?\nMinor points:\nOverall, the discussion of the paper should be more extensive, including more current references on the regulation of protein synthesis through the mTOR/MYC axis, as well as explaining the role CDK8 might play in this process.", "reviewer_3": "This manuscript by Wang et al. investigated the role of CDK8 in MYC-driven medulloblastoma, particularly focused on Group 3 MB, which had a notably poor prognosis. Through CRISPR-Cas9 screening, the authors identified CDK8 as a critical vulnerability in MYC-driven MB. They demonstrated that CDK8 regulated protein synthesis through transcriptional control of ribosomal genes and showed its cooperation with MYC in driving tumorigenesis. The study reveals that CDK8 depletion leads to decreased MYC expression, reduced protein synthesis, and suppressed tumor growth. Furthermore, they evaluated a novel CDK8 inhibitor, RVU120, showing its efficacy both alone and in combination with mTOR inhibition as a potential therapeutic strategy.\nAlthough there is a multitude of data of high quality, using numerous techniques, conceptual novelty is lacking. Targeting CDK8 and mTOR signaling have already been described in medulloblastoma and other types of brain tumors. The molecular analyses provided in this paper do employ cutting edge techniques and extensive supporting data, but many of the current figures could indeed be supplemental and don't particularly offer new or unpredictable insights.\n1. The concept that CDK8 maintains stemness and tumorigenicity through MYC is not new, and was published in Oncogene by the Hinoi lab in 2021.\n2. Similarly, targeting mTOR signaling in medulloblastoma also have been described in several studies, such as 2021, Dev Cell; 2019, Sci. Rep.\n3. in Fig. 3, 5 and 6, some panels should be attached in the supplemental figures to make the main figures clear and easy to follow.\n4. As shown in fig. 1m, knockdown of CDK8 leads to dramatically reduced tumor, why the shCDK8 mice still died 40 days post tumor cells injection? In addition, the tumor cells were injected into forebrain cortex, however, the cerebellum injection maybe more accurately mirror medulloblastoma progression.\n5. the authors did single-cell murine cerebellar transcript analysis to check CDK8 expression pattern in different cell types, and performed single cell analysis for G3 medulloblastoma tissues from seven patient samples. The human cerebellum single-cell data has already been published in Nature by Northcott lab and Taylor lab in 2022. The author may check CDK8 expression pattern in different cell types of human cerebellum.\n6. what do the right panel of Fig. 2e mean? There is no description for it in the main text and figure legend.\n7. in fig. 1g, only 8 patients in low CDK8 group, the median cutoff mode maybe more convincible for survival analysis.\n8. Many sentences need to be revised.\nPage2 line3, “Medulloblastoma (MB) is the most common malignant pediatric tumor” should be revised by “Medulloblastoma (MB) is the most common malignant pediatric brain tumor”\nPage2 line6, “resulting in long-term survival rates of less than 5%” should be revised by “resulting in long-term survival rates of less than 50%”\nPage9 line7-11, fig. 5d, fig. 5e, fig. 5f should be revised by fig. 6d, fig. 6e, fig. 6f", "reviewer_4": "In this manuscript, the authors identify CDK8 as a key regulator of MYC-driven medulloblastoma, primarily through its role in ribosome biogenesis and protein synthesis. From a translational perspective, they demonstrate that combining the CDK8 inhibitor RVU120 with the mTOR inhibitor TAK228 significantly delays tumor growth and extends survival in MYC-driven medulloblastoma xenograft models. While the study offers valuable insights into the mechanisms and therapeutic potential for MYC-driven medulloblastoma, several major concerns need to be addressed before publication.\n1. The identification of CDK8 in Fig. 1a-d is not clearly explained. The methods and strategies used to identify CDK8 are difficult to understand based on the details provided in the manuscript. For example, the X-axis in Fig. 1b and 1c is not labeled.\n2. Statistical analysis should be applied to Fig. 1f and Fig.S1c.\n3. In Fig.1g, were all subgroups of medulloblastoma (high Myc expression) considered for survival analysis? Subgroups only should be used to do survival analysis. What is the statistical method used?\n4. In Fig 1i, stem cell makers and differentiation makers should examine whether CDK8 depletion decreased stemness and increased differentiation.\n5. Considering the low (D425) or lack of (D458) KO efficacy of shCDK8#1 in Fig. 1i, it is unclear how CDK8 KO significantly decreases cell confluence in Fig. 1j. Has the possibility of off-target effects been considered?\n6. Which shCDK8 was used for in vivo experiments in Fig 1m and n? The sagittal images should be used to exhibit tumor location and pathological features. CDK8 pathway should be evaluated by IHC on these tumors.\n7. In Fig. 2e, the CDK8 protein level should be checked by WB.\n8. In Fig 2 j, what is SEL 120? Is it similar to the RVU120 compound?\n9. Why is the NHA cell line not sensitive to the CDK8 inhibitor, despite its high CDK8 levels as shown in Fig. S2a?\n10. CDK8 deletion leads to a significant reduction in chromatin occupancy at promoter regions of genes associated with other pathways, such as neurogenesis. The expression of these genes in Fig. 5h should be further validated. Additionally, why are these genes (SMARCD3, NEFL, and RELN) considered to be involved in neurogenesis?\n11. Considering that CDK8 downregulates the mTOR signaling pathway and that mTOR and CDK8 inhibitors have similar effects, what is the rationale for combining the two for treatment?\n12. A concern with RVU120 treatment is its potential side effects. The authors noted that mice experienced weight loss with RVU120 and the combination therapy, suggesting a possible cause. This could have significant implications for its use in pediatric patients.\n13. In Fig. 7f, g, and h, the authors assess the mTOR inhibitor TAK-228, which is known for its ability to penetrate the blood-brain barrier. However, it is unclear why Torin1 was tested in the D425 and D458 cell lines in Fig. 8. Both inhibitors should be validated in these cell lines both in vitro and in vivo for rigorous evaluation.\n14. In this paragraph, the figure labels should refer to Figure 6, not Figure 5. “Similar chromatin alterations in Pol II and phospho-Pol II were 7 observed in both cytosolic and mitochondrial ribosomal genes following CDK8 knockdown (Fig. 5d). These chromatin changes were associated with ribosomal gene expression, as evidenced 9 by the overlap peak track of CUT&RUN and RNA-seq (Fig. 5e). The differential alterations in Pol 10 II and phospho-Pol II peaks significantly contributed to various pathways associated with mRNA 11 translation, including rRNA metabolic processes and ribosome biogenesis (Fig. 5f). ”"}, "author_response": "Rajeev Vibhakar, Professor\nDepartment of Pediatrics\nHematology/Oncology/Bone Marrow Transplant\nUniversity of Colorado Anschutz Medical Campus\nResearch Complex 1, North Tower\n12800 E. 19th Ave. MS 8302\nAurora, CO 80045\nPhone: (303) 724-4612\nFax: (303) 724-4015\nEmail: Rajeev.Vibhakar@cuanschutz.edu\nApril 25th, 2025\nDear Reviewers,\nPlease find attached a revised version of our manuscript titled “Transcriptional regulation of Ribosome\nBiogenesis by Mediator Kinase in MYC-driven medulloblastoma” for your consideration for publication in Nature\nCommunications. We sincerely thank your thoughtful suggestions and constructive comments, which have\nhelped us improve the manuscript. In response, we have completed the requested revisions, revised and edited\nthe language, and reformatted the figures accordingly.\nA detailed point-by-point response to the reviewers' comments is provided below.\nReviewer #1 (Remarks to the Author):\nWang and coworkers are presenting an interesting manuscript that describes the importance of the kinase CDK8\nand targeting strategies against CDK8 in pediatric cancer. A certain focus is dedicated towards MYC-driven\nmedulloblastoma (MB), a very aggressive pediatric brain tumor with poor prognosis. The manuscript is well\nwritten and the presented experimental data is comprehensive, reasonable and rational. The authors have used\nseveral methods and preclinical techniques providing compelling evidence for the role of CDK8 in MYC-driven\nGroup 3 MB maintenance and proof-of-concept data for a promising CDK8 inhibitor that can be used as a\npotential treatment for this MB subgroup. Still, a few questions and concerns remain and can be summarized\nhere below:\nMajor points:\n1. Authors state that MB subgroups 3 beta and 3 gamma have higher CDK8 levels, likely because of high MYC\nlevels. It is shown in Fig. 1g that CDK8 expression correlate with poor survival, but it is also evident that some\nSHH tumors present with high CDK8 levels. Given that SHH MBs have high levels of MYCN (that shows a\nmutually exclusive expression pattern as compared to MYC), it would be interesting to see if CDK8 correlates\nwith MYCN as well. For example, Chipumuro et al. Cell, 2014 suggests that shCDK8 reduces MYCN levels and\ncorrelating gene expression of CDK8 with MYC and/or MYCN (in bulk or single RNA-Seq data) would be useful\nand easy to include. Further, CDK8 and CDK19 would likely anti-correlate in specific subgroups of patients. Such\ndata is however never shown.\nResponse: We thank the reviewer for this comment. We agree that investigating the relationship between\nCDK8/CDK19 and MYC/MYCN is important. To address this, we performed correlation analyses using the bulk\nRNA-seq dataset from Cavalli et al. (Cancer Cell, 2017). These analyses revealed no significant positive\ncorrelation between CDK8 or CDK19 expression and MYC or MYCN levels across medulloblastoma subgroups\n(Fig. 3d). Loss of MYC did not alter CDK8 expression (Fig. 3f). While CDK8 expression itself does not appear to\nbe directly regulated by MYC, our data support the conclusion that MYC requires CDK8 to sustain the oncogenic\ntranscriptional program that drives tumor growth. Targeting CDK8 disrupts MYC-driven transcriptional networks.\nThe elevated CDK8 expression observed in MYC-high medulloblastoma is likely a consequence of the globally\nhyperactive transcriptional state, rather than direct regulation by MYC. This suggests a form of non-oncogene\naddiction, where tumor cells become dependent on auxiliary transcriptional regulators like CDK8 to sustain\noncogenic programs and ensure survival. We now provide a comprehensive set of data to further interpret the\nrelationship between CDK8 and MYC (Fig. 3) and the results section has been updated accordingly.\n2. If CDK8 is low in normal cells CDK8 could be regulated by increased MYC levels in these tumors? If CDK8\nitself is not amplified in patients it must be epigenetically activated or turned on by MYC. To show if MYC\nregulates CDK8 levels knocking MYC out in cells would lead to decreased levels of CDK8. This would be an\nimportant experiment that would improve the understanding of the CDK8-MYC relationship in this story.\nResponse: Yes, CDK8 expression is lower in normal cells compared to Group 3 medulloblastoma cells. We\nperformed immunofluorescence analysis comparing normal human astrocytes to G3-MB cell lines D458 and\nMB002 and observed elevated CDK8 expression in the tumor cells (Fig. 1f and Supplementary Fig. 1c, d).\nAdditionally, MYC ChIP-seq in MB cells showed no MYC-binding peaks at the CDK8 promoter region (Fig. 3e),\nand MYC knockdown did not alter CDK8 expression (Fig. 3f). Together, these results suggest that CDK8 is not\ndirectly regulated by MYC in G3-MB.\n3. RVU120 data show promising results in cell killing of Group 3 MB cell lines. Most lines have MYC amplification\nor overexpression. In Fig 2b-e, apart from NHAs including a MYC low MB cell line would have been useful in\norder to understand the specificity of RVU120 on MYC-driven cancers. Again, it is not clear if RVU120 would\ninhibit MYC levels in these lines.\nResponse: We tested the IC50 values of RVU120 in low-MYC MB cell lines DAOY and ONS76 (Fig. 2b). RVU120\nshowed much higher IC50s in these cells, similar to those observed in normal human astrocytes, indicating that\nMYC-high MB cells are more sensitive to CDK8 inhibition. To test whether MYC directly affects this sensitivity,\nwe transfected MYC into DAOY and ONS76 cells and observed a marked reduction in IC50 compared to the\nparental cells (Fig. 4k, m). RVU120 treatment did not reduce MYC protein levels in MB cells (Supplementary Fig.\n4a, b) —likely due to compensatory feedback mechanisms that promote MYC upregulation. Despite this increase\nin MYC protein, RNA-seq analysis revealed that CDK8 inhibition suppresses the MYC-driven transcriptional\nprogram (Supplementary Fig. 4c).\n4. I am happy to see that more clinically relevant PDX models are included and presented in Fig. 2k. However,\nwhy are not overall survival (OS) shown in these models after RVU120 treatment? If survival is not significant\nthis should be stated.\nResponse: This experiment was designed to examine the effects of RVU120 and to generate xenograft tissue\nfor single-cell RNA sequencing. We treated the mice with RVU120 for two weeks and sacrificed them on the\nsame day for tissue collection. Unfortunately, we were unable to obtain high-quality nuclei suitable for scRNA-\nseq analysis.\n5. There is no discussion of toxicity and/or side effects of knockdown, depletion or inhibition of CDK8, which\nshould also be very essential. For RUV120, hematology toxicity is tested which is good. Still, using RVU120 in\ncombination with radiation treatment or standard chemotherapy would also be both useful and convincing for\nfuture upcoming trials.\nResponse: We did not observe any significant toxicity or side effects associated with CDK8 knockdown in\nxenograft models. Body weight remained stable until the final few days of the study. We repeated this experiment\nin a small independent cohort and similarly did not detect hematologic toxicity. Further, RVU120 has been tested\nin phase 1 clinical trials in humans (NCT04021368, NCT06268574, NCT06397313) with minimal toxicity largely\nlimited to grade 2 GI toxicity. RVU120 is currently in phase 2 clinical trials in combination studies (NCT06191263)\nand preliminary data published also show acceptable toxicity. We have clarified the discussion and included\nreferences to these trials. We agree that combining RVU120 with radiation or standard chemotherapy could be\nvaluable in future studies. Our current work is focused on characterizing the effect of CDK8 inhibition in MYC-\ndriven Group 3 medulloblastoma and its role in protein synthesis. Investigating combination strategies is a logical\nnext step and indeed these studies have been initiated.\n6. The CDK8 kinase is a part of the Mediator complex, which regulates gene transcription by RNA polymerase\nII. Thus, it is not surprising that ribosome biogenesis genes are regulated when manipulating CDK8 levels. In\nFig 6. RVU120 shows an effect of p-Pol2. As an important comparison in several panels, other RNA Pol inhibitors\n(α-amanitin or actinomycin D) could have be tested as a positive control.\nResponse: We thank the reviewer for this comment. Actually, we observed that CDK8 loss led to a greater\nnumber of significantly upregulated genes compared to downregulated ones (523 genes vs. 66 genes) (Fig. 3h).\nOur data indicate that ribosomal gene expression is more dependent on CDK8 than on other cyclin-dependent\nkinases involved in global transcription. For example, although CDK7 broadly regulates transcription, CDK7 loss\ndid not result in a comparable reduction in ribosome biogenesis–associated gene expression (Fig. 4i). These\nfindings highlight a more specific and essential role for CDK8 in regulating ribosomal gene transcription.\nWe re-evaluated total RNA Polymerase II (Pol II) and phospho-Pol II (Ser2) levels in D458 and MB002 cells\ntreated with RVU120 at concentrations of 500 nM, 1000 nM, and 2000 nM, and observed a decrease in phospho-\nPol II levels at 24 hours (Fig. 7f). Additionally, we performed immunoblotting on D458 and MB002 cells treated\nwith α-amanitin (1 μg/ml, 24 h) and found no reduction in total Pol II protein levels. Given that our study focuses\non CDK8, we did not pursue longer treatment times or higher doses of α-amanitin.\n7. That mTOR inhibition is a useful targeting strategy for these tumors is well known and expected. The data on\nsynergy of mTOR inhibition and RVU120 is convincing. Still, RVU120 in combination with chemotherapies\n(cisplatin or cyclophosphamide) or radiation therapy is never presented. Such data would be important to show,\nas if they are showing good response/synergy it might be worth combining CDK8 inhibitors with standard of care.\nResponse: mTOR inhibition has been extensively studied across various tumor models. In medulloblastoma,\nmost studies have focused on the SHH subtype, with several reports demonstrating its efficacy in this context.\nSeveral studies have evaluated the efficacy of dual PI3K–mTOR inhibitors in G3-MB. Only one study to date has\nspecifically evaluated mTOR inhibition specifically in G3-MB, reporting no significant effect in the D425 xenograft\nmodel after a one-week treatment, but showing a survival benefit in the MED211 model (Rachael et al., Cancer\nLetter 2021). We demonstrated that the brain-penetrant mTOR inhibitor TAK228, administered at 1.0 mg/kg via\noral gavage in 0.5% NMP and 0.5% PVP, significantly extended survival in both D425 and D458 G3-MB\nxenograft models. Moreover, combining TAK228 with CDK8 inhibition further improved survival outcomes.\nNotably, a clinical trial developed by us that evaluates this combination strategy (PBTC Trial C180) was recently\napproved by the Pediatric Brain Tumor Consortium scientific committee and is in full development in collaboration\nwith RVYU Therapeutics. In future studies, we will further investigate whether CDK8 contributes to DNA damage\nresponse and evaluate the potential synergy of CDK8 inhibition with radiation and chemotherapies.\nMinor points\n1. In Fig. 1e (and Fig. 4b) single cell data is used from MB patients. It is unclear in text and methods if this is\nalready published data or newly generated data.\nResponse: The single-cell data were obtained from our previously published study and were re-analyzed for this\nmanuscript. We have now revised the Results section to clarify this point.\n2. Is the cohort described in Fig. 1f from Cavalli et al.? If so, this set is not properly cited.\nResponse: Yes. We have revised the figure legend to include the proper citation.\n3. When describing how CDK8 regulate MYC levels in Fig 1i, it would be important to refer to the Fukusawa et\nal. paper that already showed this relationship in other brain tumors.\nResponse: Thank you for the suggestion. We have now cited the study by Fukusawa et al. in the corresponding\nsection to acknowledge the previously reported relationship between CDK8 and MYC regulation in other brain\ntumors.\n4. Labeling and arrangement of panels are not optimal for Figure 1. In general, some Figure legends are not\nalways as informative as main text, and should be improved.\nResponse: Thank you for the suggestion. We have re-arranged the panels in Figure 1 to improve clarity and\nreadability, and we have revised the figure legend to provide more detailed and informative descriptions.\n5. In Fig. 2e. to the left, the concentration of RVU120 is not indicated. In Fig 2e. to the right: no time point\nindicated when the lysate was collected. That there was changes in pSTAT1/STAT1 ratio in D425, but not in\nD458 is not discussed.\nResponse: Thank you for pointing this out. We have added the dose and time point information in figure legend,\nas well as quantification of the immunoblot. The pSTAT1/STAT1 ratio was decreased in both D425 and D458\ncell lines.\n6. Related to above, number of cell lines are listed in the method section without any data, and selection of D425\nand D458 as representative cell lines is not motivated; the difference in results and potential underlying genetic\ncauses in D425 and D458 results are not discussed.\nResponse: D425, D458, D341, and HDM03 are all high-MYC medulloblastoma cell lines. Additionally, we have\nincorporated data from an additional Group 3 line, MB002, in several assays, including the OPP assay, shMYC\nknockdown, immunoblots, and EU incorporation experiments.\n7. In Fig. 2j “SEL120” instead of RUV120 is written.\nResponse: “SEL120” was the former name for RVU120. We have now corrected the label in the figure to ensure\nconsistency.\n8. In Fig. 6, the results are inconsistent. Thus, I do not get to conclude that “RVU120 is more efficient” if the result\nis not the same? Please clarify and modify your conclusion related to the data shown in:\nFig. 6a. decreased ppol2 and pol2 (RVU120)\nFig. 6b. increased pol2 and decreased ppol2 (shCDK2)\nFig. 6g. decreased ppol2 and pol2 (RVU120)\nFig. 6d. increased cytosolic and mitochondrial pol2\nFig. 6i. decreased cytosolic mitochondrial pol2 (RVU120)\nResponse: Thank you for the insightful comment. We agree that the effects of shCDK8 and RVU120 on Pol II\ndynamics are not identical. The observed differences likely reflect distinct mechanisms of action between genetic\ndepletion and pharmacologic inhibition. Specifically, shCDK8 eliminates CDK8 protein levels, disrupting its partial\nkinase activity and its scaffolding functions within the Mediator complex, which leads to Pol II pausing at\npromoters and impairs the elongation of phospho-Pol II across gene bodies. In contrast, RVU120 selectively\ninhibits CDK8’s kinase activity, resulting in reduced overall Pol II recruitment and decreased levels of both total\nand phospho-Pol II (Ser2). We have clarified this interpretation and updated the relevant conclusions in the\nResults section.\n9. For many in vivo studies, information about the administration of drug(s) is missing, e.g. duration of\nadministration and stop points. For example, in Fig. 7 and Fig. 8: RVU120 and TAK-228 are given by daily oral\ngavage. But for how long was it administrated? Does the tumor start to grow and mice start to get sick during\ntreatment?\nResponse: We initiated treatment once tumors were established, defined by a bioluminescence signal reaching\n1 to 5 × 10⁵- photons, as measured by IVIS imaging. RVU120 and TAK-228 were administered daily by oral\ngavage and continued until the mice reached endpoint criteria, which were defined by the development of severe\nsymptoms. We have now revised the manuscript to include these details regarding treatment duration and\nstopping points.\n10. In Methods under Compounds, please change to “CDK8 and mTOR inhibitors”.\nResponse: Thanks for reviewer’s suggestion. We changed it.\n11. Please present more information on the lentivirus transduction protocol used and specifics of shRNA\ntargeting in the method section?\nResponse: We have cited our previous publication detailing the lentiviral transduction protocol and included the\nshRNA IDs used to target CDK8 and MYC in the Methods section.\nReviewer #2 (Remarks to the Author):\nThe manuscript presented by Wang et al. identifies CDK8 as a critical therapeutic target in MYC-driven\nmedulloblastoma (MB), a highly aggressive brain cancer. It proposes that CDK8 regulates MYC expression,\nribosome biogenesis, and protein synthesis, driving MB tumor growth. Using inhibitors like RVU120 and\ncombining CDK8 and mTOR inhibition show potential for enhanced therapeutic efficacy in preclinical models.\nAlthough the data included in the manuscript is convincing and interesting, further experimental evidence are\nrequired to support their conclusions. I strongly believe that is important to provide much stronger evidence to\ndetermine whether CDK8 regulates protein synthesis directly or indirectly through MYC. Additionally, the authors\nshould better discuss this aspect, considering that MYC is a direct regulator of ribosome biogenesis at multiple\nlevels (this review provides references to excellent articles on the topic: https://doi.org/10.1038/nrc2819).\nMoreover, while the RNA-seq data are very robust, it would be beneficial to include an additional experiment to\ndemonstrate the impact on rRNA transcription or its processing. In addition, throughout the manuscript, the\nauthors should try to justify why they use shRNA, sgRNA or siRNA against CDK8 along the manuscript.\nResponse: We thank the reviewer for their thoughtful and constructive comments. As suggested, we have added\nnew data to strengthen our conclusions. Our results support the conclusion that CDK8 inhibition or depletion\ndisrupts ribosome biogenesis and protein synthesis, and that these effects are dependent on MYC. In addition,\nwe now include data demonstrating that CDK8 inhibition impairs rRNA synthesis, further supporting its role in\nregulating the translational machinery. We have also clarified the rationale for our use of shRNA or sgRNA to\ntarget CDK8 at different points in the manuscript. A detailed point-by-point response to the reviewer’s comments\nis provided below.\nMajor points:\n1. In Figure 1, the authors demonstrates that CDK8 is a critical therapeutic vulnerability in MYC-driven\nmedulloblastoma (MB). Through CRISPR-Cas9 screening, CDK8 was identified as essential for tumor growth,\nwith its loss reducing MYC expression, cell proliferation, and neurosphere growth. High CDK8 expression\ncorrelates with poor survival in high-MYC MB, and its inhibition in mice significantly reduced tumor growth and\nextended survival, emphasizing its potential as a therapeutic target.\n-Why in Figure 1i the authors did not validate the results by making CDK8 knock outs?. In the same figure the\nauthors mention a “notable decrease in MYC levels” (line 2; page5), however, there doesn’t seem to be much\ndifference between the two shCDK8 constructs, despite the differences in CDK8 depletion efficiency, especially\nin D425 cell line. Quantifying these levels could provide greater clarity. Validation would be more robust using\none sgRNA from the CRISPR screening library and a different one as well.\n-Importantly, there is no information on how the CRISPR screening was performed, which library was used, how\nmany sgRNAs were used per gene, or how many of the CDK8 guides indicated vulnerability in MYC-driven MB\ntumors. This information is essential in order to publish the paper.\nResponse: We appreciate the reviewer’s comments. In response, we re-selected CDK8 knockdown cells using\na higher dose of puromycin and performed immunoblotting and proliferation assays. The results were consistent\nwith those shown in Fig. 1g, h, where CDK8 knockdown significantly impaired both proliferation and neurosphere\nformation. We also validated this result using CRISPR-mediated CDK8 knockout in D458 cells, where loss of\nCDK8 led to reduced MYC protein levels, as confirmed by Western blot analysis. Quantification of CDK8 and\nMYC protein levels has been added to the revised figure (Figure 3i, j) to clarify the observed effects. Regarding\nthe CRISPR-Cas9 screen, we have now included a detailed description of the screen protocol in the revised\nMethods section.\n2. Based on the results shown in Figure 3, the authors claim that CDK8 depletion leads to significant\ndownregulation of genes associated with mRNA translation, ribosome assembly, and protein synthesis. As far\nas I know, this is the first time a direct regulation of translation by CDK8 has been suggested. Important controls\nshould be included:\n- Polysome profiling in cells treated with the inhibitor (as is sown in Fig. 4g, are required). Quantifying polysomes\nwill offer a more comprehensive and robust analysis of the impact of CDK8 depletion on the distribution between\npolysomes, monosomes, and individual ribosomal subunits (40S and 60S).\nResponse: We performed polysome profiling on MB cells treated with RVU120, which resulted in a reduction of\nthe 80S peak in monosomes. Additionally, we conducted a quantification of the ribosome profiling results, which\nhave been included in the revised manuscript (Fig. 5h).\n- Is rRNA transcription affected? EU incorporation assays would help clarify this point. The text suggests that\nCDK8 depletion might impact rRNA maturation, but this is not demonstrated. Specific PCR assays or northern\nblot should be conducted to study rRNA processing and provide evidence for this hypothesis.\nResponse: We thank the reviewer for this insightful comment. We performed EU incorporation assays in CDK8\nknockout, CDK8 knockdown, and RVU120-treated medulloblastoma cells (Fig. 5k–m).The treatment resulted in\na reduction in EU intensity, demonstrating that CDK8 inhibition suppresses newly synthesized RNA. Since\napproximately 80% of total cellular RNA is rRNA, this suggests that CDK8 inhibition also suppresses rRNA\ntranscription. We agree that PCR assays targeting the transcribed spacers (such as the 5’ETS, ITS1, or ITS2)\nand Northern blot analysis would provide additional evidence for this hypothesis. Our current focus is on the\nbroader role of CDK8 in ribosome biogenesis, and we plan to further investigate the specific impact of CDK8 on\nrRNA processing in future studies.\n- The authors suggest that CDK8 depletion might impact protein synthesis independently of MYC; however, the\nresults are not entirely convincing, and additional experiments should be included in the manuscript. For this, I\nsuggest to the authors to study the effect of a complete MYC depletion in cells treated with the CDK8 inhibitor\nor in CDK8 knockout cells, and analyze the impact in OPP, EU, rRNA processing, and survival curves.\nResponse: To determine whether the role of CDK8 in ribosome biogenesis is dependent on MYC, we first\nperformed bulk RNA-seq following MYC knockdown in Group 3 medulloblastoma cells and compared the results\nto those from CDK8 knockdown. The top gene sets affected by MYC knockdown were primarily related to\nmitochondrial function and ATP synthesis (Fig. 4h), whereas CDK8 knockdown/knockout predominantly\nimpacted gene sets involved in ribosome biogenesis and translation (Fig. 5f, g). CDK8 depletion affected a\nbroader set of genes associated with ribosome biogenesis and mRNA translation than MYC knockdown or\nknockdown of other genes that reduce MYC levels (Fig. 4i), suggesting a distinct role for CDK8 in regulating\nthese processes.\nWe then performed OPP assays in medulloblastoma cell lines with low MYC expression (ONS76 and DAOY)\ntreated with various doses of RVU120. A reduction in OPP signal was observed in DAOY cells only at 4 μM\nRVU120, and no reduction was seen in ONS76 cells (Fig. 4j and Supplementary Fig. 5d), in contrast to the\nsignificant suppression observed at 0.5–1 μM in MYC-high cells. This suggests that MYC expression sensitizes\ncells to CDK8 inhibition. To further test this hypothesis, we overexpressed c-MYC in ONS76 cells and repeated\nthe OPP assay. Upon RVU120 treatment, we observed a marked reduction in OPP signal at 4 μM, indicating\nthat suppression of protein synthesis by CDK8 inhibition is dependent on MYC (Fig. 4l). Additionally, the IC50 of\nRVU120 in MYC-low cells with c-MYC overexpression was significantly lower compared to the parental cells\n(Fig. 4k, m). Together, these results suggest that MYC enhances the sensitivity of medulloblastoma cells to\nCDK8 inhibition, and that CDK8-mediated regulation of protein synthesis is dependent on MYC activity.\n3. Regarding Figure 4g, the authors state that the observed effect on the 80S peak \"indicates that CDK8 mediates\nchanges in ribosomal activity in MYC31 MB cells and is essential for continued ribosomal subunit assembly and\noverall protein synthesis.\" However, I do not consider this difference sufficient to support such a claim. How do\nthe authors demonstrate a problem in ribosomal assembly? I do not see evidence of half-mers or a loss of the\n60S peak relative to the 40S. Another aspect of this figure that could be helpful is performing an OPP assay on\nCDK8-KO cells.\n- Regarding Figure 4h, why do the authors only discuss changes in 5.8S rRNA? As far as I know, this dye labels\nall rRNA. To support this point, a Northern blot or PCR to quantify the abundance of different rRNA fragments\nwould be necessary.\nResponse: We appreciate the reviewer’s thoughtful comments. We repeated polysome profiling in\nmedulloblastoma cells treated with RVU120 and quantified the data to clarify that CDK8 depletion or inhibition\nspecifically affects the 80S monosome peak. We have revised the conclusion to reflect that a reduction in the\n80S peak may indicate impaired ribosome loading onto mRNAs, suggesting decreased translation initiation or a\nglobal reduction in protein synthesis, but it does not necessarily imply a defect in ribosomal subunit assembly.\nTo further support CDK8’s role in translational regulation, we performed an OPP assay in CDK8 knockout cells\nas well as RVU120-treated D458 and MB002 cells and observed a reduction in protein synthesis (Fig. 4d, f).\nWe appreciate the reviewer for pointing this out. Y10B is an anti-rRNA monoclonal antibody that primarily stains\nthe nucleolus and detects ribosomal RNA, specifically targeting newly synthesized pre-rRNA. The EU\nincorporation assay demonstrated that CDK8 inhibition suppresses rRNA synthesis (Fig. 5k-m), supporting our\nconclusion that CDK8 plays a critical role in regulating ribosome biogenesis.\n4. In Figure 7d, the authors mention changes in p-4EBP1 but not in p-S6; however, this effect is not discussed\nin the text. Could the decrease in p-4EBP1 levels be independent of mTOR?. Later, with the inhibitor, both p-\n4EBP1 and p-S6 are affected at longer time points. What explanation do the authors provide for this observation?\nAdditionally, what is the viability of the cells at these time points?\nResponse: We appreciate the reviewer’s comment. The observed decrease in p-4EBP1, but not in p-S6, in CDK8\nknockdown cells may be due to incomplete depletion of CDK8. When we examined p-4EBP1 and p-S6 levels\nfollowing CDK8 knockout, we found that both were reduced (Fig. 8i). We also assessed total mTOR and\nphospho-mTOR levels and did not observe significant changes (Fig. 8j), suggesting that the alterations in p-\n4EBP1 and p-S6 upon CDK8 inhibition are mediated through the MYC–mTOR axis rather than through direct\nregulation of mTOR itself. Additionally, we repeated these experiments in D458 and MB002 cells using dose-\nand time-dependent RVU120 treatments, and observed a consistent reduction in both p-4EBP1 and p-S6 levels\n(Fig. 8f, g). The delayed effects observed at longer time points may reflect a progressive disruption of MYC-\ndriven signaling, or an adaptive cellular response. We also assessed cell viability after 48 hours of treatment and\nfound that total cell numbers decreased, while the proportion of viable cells remained unchanged\n(Supplementary Fig. 5c). This suggests that the observed changes are not due to nonspecific cytotoxicity, but\nrather reflect a targeted regulatory effect.\nMinor points:\nOverall, the discussion of the paper should be more extensive, including more current references on the\nregulation of protein synthesis through the mTOR/MYC axis, as well as explaining the role CDK8 might play in\nthis process.\nResponse: We agree with the reviewer. It is novel that CDK8 inhibition disrupts the MYC/mTOR axis. Within the\ncontext of space limitations, we have expanded the Discussion and added additional references as suggested.\nReviewer #3 (Remarks to the Author):\nThis manuscript by Wang et al. investigated the role of CDK8 in MYC-driven medulloblastoma, particularly\nfocused on Group 3 MB, which had a notably poor prognosis. Through CRISPR-Cas9 screening, the authors\nidentified CDK8 as a critical vulnerability in MYC-driven MB. They demonstrated that CDK8 regulated protein\nsynthesis through transcriptional control of ribosomal genes and showed its cooperation with MYC in driving\ntumorigenesis. The study reveals that CDK8 depletion leads to decreased MYC expression, reduced protein\nsynthesis, and suppressed tumor growth. Furthermore, they evaluated a novel CDK8 inhibitor, RVU120, showing\nits efficacy both alone and in combination with mTOR inhibition as a potential therapeutic strategy.\nAlthough there is a multitude of data of high quality, using numerous techniques, conceptual novelty is lacking.\nTargeting CDK8 and mTOR signaling have already been described in medulloblastoma and other types of brain\ntumors. The molecular analyses provided in this paper do employ cutting edge techniques and extensive\nsupporting data, but many of the current figures could indeed be supplemental and don't particularly offer new\nor unpredictable insights.\nResponse: We appreciate the reviewer’s comment. To our knowledge, and as also noted by Reviewer 2, this is\nthe first manuscript to describe the role of CDK8 in regulating protein synthesis in MYC-driven tumors.\n1. The concept that CDK8 maintains stemness and tumorigenicity through MYC is not new, and was published\nin Oncogene by the Hinoi lab in 2021.\nResponse: We thank the reviewer for this comment. The referenced Oncogene paper by the Hinoi et al.\n(Oncogene, 2021) focused on the role of CDK8 in maintaining stemness in glioblastoma. In contrast, our study\ninvestigates the role of CDK8 in regulating ribosome biogenesis in MYC-driven medulloblastoma, which\nrepresents a novel and underexplored mechanism. While CDK8’s role in stemness has been described, its link\nto ribosome biogenesis and translation control in this context has not been previously reported.\n2. Similarly, targeting mTOR signaling in medulloblastoma also have been described in several studies, such as\n2021, Dev Cell; 2019, Sci. Rep.\nResponse: While previous studies have explored mTOR signaling in medulloblastoma—both focused specifically\non the SHH subgroup. In contrast, our study targets MYC-driven Group 3 medulloblastoma, which is biologically\ndistinct and associated with a more aggressive clinical course. Notably, only one prior study has examined mTOR\ninhibitor (not dual PI3K and mTOR inhibitor) in Group 3 medulloblastoma. In that study, one week of mTOR\ninhibitor treatment showed no significant effect in the D425 model, while a survival benefit was observed in the\nPDX211 model (Rachael et al., Cancer Letter 2021). In our study, we demonstrate that TAK-228, formulated in\n0.5% NMP and 0.5% PVP and administered daily via oral gavage, significantly prolongs survival in both D425\nand D458 Group 3 medulloblastoma xenograft models. Furthermore, we provide mechanistic and therapeutic\nevidence supporting the combined inhibition of CDK8 and mTOR—a combination that, to our knowledge, has\nnot been previously investigated in this disease context.\n3. in Fig. 3, 5 and 6, some panels should be attached in the supplemental figures to make the main figures clear\nand easy to follow.\nResponse: Thanks for the suggestion. To improve clarity and readability, we have reorganized several figures\nand moved some figures to the supplementary martials.\n4. As shown in fig. 1m, knockdown of CDK8 leads to dramatically reduced tumor, why the shCDK8 mice still died\n40 days post tumor cells injection? In addition, the tumor cells were injected into forebrain cortex, however, the\ncerebellum injection maybe more accurately mirror medulloblastoma progression.\nResponse: We thank the reviewer for these insightful comments. While CDK8 knockdown significantly reduced\ntumor growth, it did not completely eliminate tumor-initiating cells. As a result, the tumors eventually progressed,\nleading to animal death approximately 40 days after injection. This is consistent with previous findings where\nknockdown of essential genes—such as SOX11, BRD4, CDK7, CTNNB1, and EZH2—slowed tumor growth but\ndid not completely prevent progression.\nWe would like to clarify that the tumors were not injected into the forebrain cortex. The original text for Fig. 1m\nstates: “MB cells transduced with either a control shRNA (shNull) or shRNAs targeting CDK8 (shCDK8) were\nimplanted intracranially into immunodeficient mice”. In fact, tumor cells were injected into the cerebellum, which\nmore accurately models the anatomical origin of medulloblastoma. Our coordinates for medulloblastoma\ninjections are: 1.500 mm M/L (right) and -2.000 mm A/P (posterior) with respect to lambda and -3.000 mm D/V\n(deep) with respect to the surface of the skull at lambda. We have included the specific coordinates in the\nMethods. We have also included MRI images demonstrating tumor growth in the cerebellum (Fig. 1m).\n5. the authors did single-cell murine cerebellar transcript analysis to check CDK8 expression pattern in different\ncell types, and performed single cell analysis for G3 medulloblastoma tissues from seven patient samples. The\nhuman cerebellum single-cell data has already been published in Nature by Northcott lab and Taylor lab in 2022.\nThe author may check CDK8 expression pattern in different cell types of human cerebellum.\nResponse: We thank the reviewer for this helpful suggestion. We have attempted to access the human\ncerebellum single-cell datasets published by the Northcott and Taylor labs (Nature, 2022); however, these\ndatasets are hosted on controlled-access cloud platforms (the European Genome-Phenome Archive and St.\nJude Cloud). As of now, we have not been granted access to either dataset. We believe our current datasets\nsufficiently demonstrate that CDK8 is consistently expressed across the majority of cell populations within Group\n3 medulloblastoma, supporting its relevance in this tumor subtype.\n6. what do the right panel of Fig. 2e mean? There is no description for it in the main text and figure legend.\nResponse: The right panel of Fig. 2e shows levels of phospho-STAT1 (S727), a known direct substrate of CDK8,\nfollowing dose-dependent treatment with the CDK8 inhibitor RVU120. This panel serves to confirm that CDK8\ninhibition is effective. We have added a description of this panel to clarify its purpose.\n7. in fig. 1g, only 8 patients in low CDK8 group, the median cutoff mode maybe more convincible for survival\nanalysis.\nResponse: In Figure 1g, only 8 patients were included in the low CDK8 group because we selected the cutoff by\nscanning p-values across different thresholds during the survival analysis. We also performed the analysis using\nthe median cutoff, which similarly showed that higher CDK8 expression is associated with poorer survival,\nalthough the p-value was 0.305. This does not alter our conclusion that CDK8 is highly expressed in Group 3\nmedulloblastoma compared to normal tissue and that targeting CDK8 can disrupt the MYC-driven translation\nprogram. We have now updated the corresponding figure accordingly (Fig. 3c).\n8. Many sentences need to be revised. Page2 line3, “Medulloblastoma (MB) is the most common malignant\npediatric tumor” should be revised by “Medulloblastoma (MB) is the most common malignant pediatric brain\ntumor”. Page2 line6, “resulting in long-term survival rates of less than 5%” should be revised by “resulting in\nlong-term survival rates of less than 50%”. Page9 line7-11, fig. 5d, fig. 5e, fig. 5f should be revised by fig. 6d,\nfig. 6e, fig. 6f.\nResponse: We appreciate the reviewer’s attention to detail. We have carefully reviewed the manuscript and\nrevised the sentences mentioned by the reviewer. Additionally, we have corrected the corresponding figure\nreferences.\nReviewer #4 (Remarks to the Author):\nIn this manuscript, the authors identify CDK8 as a key regulator of MYC-driven medulloblastoma, primarily\nthrough its role in ribosome biogenesis and protein synthesis. From a translational perspective, they demonstrate\nthat combining the CDK8 inhibitor RVU120 with the mTOR inhibitor TAK228 significantly delays tumor growth\nand extends survival in MYC-driven medulloblastoma xenograft models. While the study offers valuable insights\ninto the mechanisms and therapeutic potential for MYC-driven medulloblastoma, several major concerns need\nto be addressed before publication.\n1. The identification of CDK8 in Fig. 1a-d is not clearly explained. The methods and strategies used to identify\nCDK8 are difficult to understand based on the details provided in the manuscript. For example, the X-axis in Fig.\n1b and 1c is not labeled.\nResponse: We thank the reviewer for pointing this out. We have revised the manuscript to include additional\ndetails describing the CRISPR-Cas9 screen and DepMap analysis used to identify CDK8. Additionally, we have\nupdated Figure 1 and its legend to include appropriate axis labels and improve clarity.\n2. Statistical analysis should be applied to Fig. 1f and Fig.S1c.\nResponse: We now include statistical analyses for all relevant data. Appropriate statistical tests have been\napplied, and the corresponding figure legends have been updated to reflect these changes.\n3. In Fig.1g, were all subgroups of medulloblastoma (high Myc expression) considered for survival analysis?\nSubgroups only should be used to do survival analysis. What is the statistical method used?\nResponse: In the original analysis, we used Group 3b and 3r medulloblastoma subtypes, which are characterized\nby high MYC expression. In response to the reviewer’s suggestion, we have now included survival analyses for\nthe two individual Group 3 subtypes with high MYC expression (Fig. 3c). The log-rank (Mantel-Cox) test was\nused for all survival analyses, and this has been added to the figure legend and Methods section.\n4. In Fig 1i, stem cell makers and differentiation makers should examine whether CDK8 depletion decreased\nstemness and increased differentiation.\nResponse: Thank you for the suggestion. We performed immunoblotting to assess stemness and differentiation\nmarkers in cells expressing shCDK8. However, we did not observe changes in SOX2, a marker of neural\nstemness, or GFAP, a marker of glial differentiation.\n5. Considering the low (D425) or lack of (D458) KO efficacy of shCDK8#1 in Fig. 1i, it is unclear how CDK8 KO\nsignificantly decreases cell confluence in Fig. 1j. Has the possibility of off-target effects been considered?\nResponse: To address this concern, we re-selected the shCDK8-expressing cells using a higher dose of\npuromycin and repeated the proliferation assay using the Incucyte system. CDK8 knockdown resulted in a\nsignificant reduction in medulloblastoma cell proliferation after four days. RNA-seq analysis and immunoblotting\nconfirmed a decrease in CDK8 levels in shCDK8 cells (Supplementary Fig. 1f and Fig. 3i). Regarding potential\noff-target effects, we observed consistent changes in key gene sets, particularly those related to ribosome\nbiogenesis—the main focus of this study—across CDK8 knockdown, knockout, and pharmacological inhibition\nconditions in multiple MB cell lines, supporting the specificity of our findings. Moreover, differences in shRNA\nknockdown efficiency are likely due to their targeting of distinct regions of the mRNA, which may vary in\naccessibility and susceptibility to RNA-induced silencing.\n6. Which shCDK8 was used for in vivo experiments in Fig 1m and n? The sagittal images should be used to\nexhibit tumor location and pathological features. CDK8 pathway should be evaluated by IHC on these tumors.\nResponse: We thank the reviewer for their comment. We used shCDK8 #1 (TRCN0000350344) for the in vivo\nexperiments shown in Figure 1, and this information has now been added to the revised Methods section. To\nfurther validate our findings, we repeated the in vivo experiment in a small cohort (n = 3 per group), with mice\nsacrificed when the control group reached endpoint criteria. Immunofluorescence staining confirmed reduced\nCDK8 expression in tumors from mice injected with shCDK8-expressing D458 cells (Fig. 1l). Additionally, MRI\nwas performed on the same day to monitor tumor size, and sagittal images have been included to better illustrate\ntumor location and size (Fig. 1m). These new data have been incorporated into the revised manuscript.\n7. In Fig. 2e, the CDK8 protein level should be checked by WB.\nResponse: We examined CDK8 protein expression by Western blot following RVU120 treatment at various time\npoints and concentrations in medulloblastoma cells. We observed a transient increase in CDK8 levels at early\ntime points, followed by a reduction at 48 hours (Fig. 2d and Supplementary Fig. 3a, b). Since kinase activity is\ncommonly evaluated by assessing the phosphorylation level of direct substrates, we assessed phosphorylation\nof STAT1, a known CDK8 target. As shown in Fig. 2e and 2f, phosphorylation of STAT1 decreased following\nRVU120 treatment.\n8. In Fig 2 j, what is SEL 120? Is it similar to the RVU120 compound?\nResponse: We thank the reviewer for noting the naming inconsistency. SEL120 and RVU120 refer to the same\ncompound. For clarity and consistency, we have updated the manuscript to use the name RVU120 throughout.\n9. Why is the NHA cell line not sensitive to the CDK8 inhibitor, despite its high CDK8 levels as shown in\nSupplementary Fig. 2a?\nResponse: The original Supplementary Fig. 2a was intended to show the localization of CDK8 and was not\nexposed under identical conditions across cell lines. We re-performed immunofluorescence in NHA, D458, and\nMB002 cells using two different CDK8 antibodies. These experiments confirmed that CDK8 expression is higher\nin Group 3 medulloblastoma cells compared to NHA cells (Fig. 1f and Supplementary Fig. 1c, d). The new data\nand corresponding figures have been added to the revised manuscript.\n10. CDK8 deletion leads to a significant reduction in chromatin occupancy at promoter regions of genes\nassociated with other pathways, such as neurogenesis. The expression of these genes in Fig. 5h should be\nfurther validated. Additionally, why are these genes (SMARCD3, NEFL, and RELN) considered to be involved in\nneurogenesis?\nResponse: Thank you for the comment. We did not observe significant changes in the expression of these genes\nfollowing CDK8 depletion. However, the loss of CDK8 may create a chromatin state that is \"poised for\nactivation\"—in which genes are primed but not yet transcriptionally active, awaiting further regulatory signals.\nSMARCD3, NEFL, and RELN were selected based on gene ontology analysis, which revealed enrichment in\nneurogenesis-related pathways. Specifically, SMARCD3 is a chromatin remodeler involved in cellular\ndifferentiation, including neuronal lineages; NEFL is a marker of neuronal maturation and axonal development;\nand RELN is essential for neuronal migration and brain development.\n11. Considering that CDK8 downregulates the mTOR signaling pathway and that mTOR and CDK8 inhibitors\nhave similar effects, what is the rationale for combining the two for treatment?\nResponse: We appreciate the reviewer’s thoughtful question. Although CDK8 inhibition downregulates\ncomponents of the mTOR signaling pathway and both CDK8 and mTOR inhibitors show similar phenotypic\neffects, our rationale for combining them is based on the potential for additive or synergistic inhibition of\noverlapping yet non-identical pathways. CDK8 regulates transcriptional programs that impact upstream and\ndownstream effectors of mTOR, while mTOR inhibitors act more directly on translational machinery and\nmetabolic control. By targeting both transcriptional and post-transcriptional layers, the combination may produce\na more robust and sustained suppression of tumor growth. Our data support this by showing enhanced effects\non proliferation and protein synthesis when both inhibitors are used together. Additionally, targeting two distinct\npathways may reduce the likelihood of resistance, leading to more durable and effective therapeutic responses.\n12. A concern with RVU120 treatment is its potential side effects. The authors noted that mice experienced\nweight loss with RVU120 and the combination therapy, suggesting a possible cause. This could have significant\nimplications for its use in pediatric patients.\nResponse: We thank the reviewer for raising this important point. RVU120 has generally been considered well-\ntolerated in early-phase clinical trials, with no consistent reports of major off-target toxicities such as cardiotoxicity\nor neurotoxicity. For example, RVU120 has been tested in phase 1 clinical trials in humans (NCT04021368,\nNCT06268574, NCT06397313) with minimal toxicity largely limited to grade 2 GI toxicity. RVU120 is currently in\nphase 2 clinical trials in combination studies (NCT06191263) and preliminary data published also so acceptable\ntoxicity. We have clarified the discussion and included references to these trials. These trials are ongoing, and\nadditional safety data will continue to emerge as dose-escalation and expansion cohort’s advance.\n13. In Fig. 7f, g, and h, the authors assess the mTOR inhibitor TAK-228, which is known for its ability to penetrate\nthe blood-brain barrier. However, it is unclear why Torin1 was tested in the D425 and D458 cell lines in Fig. 8.\nBoth inhibitors should be validated in these cell lines both in vitro and in vivo for rigorous evaluation.\nResponse: Torin 1 is a tool compound and does not efficiently cross the blood–brain barrier (BBB), which is why\nwe selected TAK-228 for our in vivo studies. Torin 1 was initially used in early in vitro experiments to assess\npotential synergy with RVU120 as a proof of principle in targeting these pathways. However, due to its limited\nBBB permeability, we transitioned to TAK-228 for in vivo validation. In response to the reviewer’s suggestion,\nwe have now evaluated TAK-228 in vitro in both cell lines (Supplementary Fig. 9b, c). Additionally, we performed\nadditional in vivo studies using the D425 xenograft model. Treatment with TAK-228 (formulated in 0.5% NMP\nand 0.5% PVP and administered daily via oral gavage) significantly extended survival in mice bearing D425\ntumors (Fig. 8c). These new data have been incorporated into the revised manuscript.\n14. In this paragraph, the figure labels should refer to Figure 6, not Figure 5. “Similar chromatin alterations in Pol\nII and phospho-Pol II were 7 observed in both cytosolic and mitochondrial ribosomal genes following CDK8\nknockdown (Fig. 5d). These chromatin changes were associated with ribosomal gene expression, as evidenced\n9 by the overlap peak track of CUT&RUN and RNA-seq (Fig. 5e). The differential alterations in Pol 10 II and\nphospho-Pol II peaks significantly contributed to various pathways associated with mRNA 11 translation,\nincluding rRNA metabolic processes and ribosome biogenesis (Fig. 5f). ”\nResponse: We thank the reviewer for pointing out this oversight. The figure labels in this paragraph were\nincorrect. We have corrected the citations to refer to correct figure in the revised manuscript.\nWe thank you for your consideration of our work.\nSincerely,\nRajeev Vibhakar MD, PhD\nCancer League of Colorado Chair\nDr. Nicholas Foreman Chair in Pediatric NeuroOncology Research\nProfessor of Pediatrics and Neurosurgery\nMorgan Adams Pediatric Brain Tumor Research Program\nUniversity of Colorado School of Medicine\nCenter for Cancer & Blood Disorders, Children’s Hospital Colorado"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the efforts by the authors to resolve most of the concerns we had with this manuscript. Happy to see supporting data that CDK8 is not directly regulated by MYC. Good also that references to important clinical trials on RVU120 has been included in the story and that overall survival of RVU-120 is shown in Fig. 2k. Finally, inclusion of more OPP assays and overexpression of MYC in non-MYC amplified lines have improved this story. It is clear that OPP assays are useful but sometimes they are not clearly visualized if printed in a PDF format (but can be seen in high quality source figures). Would thus be useful to include or have access to high quality figures online.\nI also appreciate the inclusion of data/references on how the scRNA Seq analysis was performed. However, another point of concern in Figure 3b is how the new RNA Seq data of normal cerebellar samples generated by the authors was included and compared to an existing microarray dataset. What normal cases are included (bulk RNA preparation, sequencing platform (microarray or RNA Seq?), number of samples, age, region?). And perhaps more importantly, how were these samples batch normalized to the existing Cavalli set of MB samples? Further, is this data comparable with other data sets published where normal cerebellar samples are included? Apart from this concern I have no further comments.", "reviewer_2": "Although I appreciate the authors' efforts to discuss all the points raised, I still consider that important controls are missing to validate the conclusions proposed in the manuscript. In addition, I suggest that the figures and text be reorganised to make the manuscript easier to follow. Many of the figures could become supplementary figures.Reviewer response: The answer provided by the authors is not sufficient to understand whether CDK8-mediated regulation of protein synthesis is dependent on Myc regulation or not. I believe that the proposed experiments should be included in the manuscript.\n2. Based on the results shown in Figure 3, the authors claim that CDK8 depletion leads to significant downregulation of genes associated with mRNA translation, ribosome assembly, and protein synthesis. As far as I know, this is the first time a direct regulation of translation by CDK8 has been suggested. Important controls should be included:\n- Polysome profiling in cells treated with the inhibitor (as is sown in Fig. 4g, are required). Quantifying polysomes will offer a more comprehensive and robust analysis of the impact of CDK8 depletion on the distribution between polysomes, monosomes, and individual ribosomal subunits (40S and 60S).", "reviewer_3": "The revised manuscript from Wang and colleagues has been substantially improved. The authors have carefully considered the points raised in the initial review, and they have provided detailed responses to each with additional data or clarification. The manuscript provides comprehensive and convincing evidence of the potential of inhibition of CDK8-mediated transcriptional regulation and mRNA translation as a promising therapeutic approach for MYC-driven MB.\nI have only one remaining comment with regard to the revised manuscript. The authors state “In fact, tumor cells were injected into the cerebellum” in Fig. 1m in the revised manuscript. Nevertheless, the Fig. 1n still used the forebrain images, which exhibited the obvious forebrain structures, such as hippocampus. The authors should check and confirm it.", "reviewer_4": "The authors addressed all my questions and concerns. I do not have further comments."}, "author_response": "Second Round Response: We appreciate the reviewer’s comments. In response, we have zoomed in on the panels with fewer visible cells to improve clarity. All immunofluorescence images, including those for the OPP assays, have been submitted at high resolution to ensure optimal visibility.\nIn Figure 3b, we compared gene expression data from the Cavalli et al. medulloblastoma dataset (generated using the Affymetrix U133 Plus 2.0 microarray platform) with normal cerebellar samples collected in our institute and profiled using the Affymetrix Gene 1.1 ST array. To integrate data across the two platforms, we employed a normalization strategy previously described in Northcott et al., Nature 2014. Specifically, for each platform, we calculated a contrast value per gene by subtracting the mean expression of that gene across all samples within that platform. These platform-specific contrast values were then merged to generate a unified dataset suitable for comparative analysis. The description of this normalization approach is provided in the Methods section.\nGiven the absence of ideal controls for the molecular heterogeneity of medulloblastoma, pediatric normal cerebellum remains the most appropriate reference for comparative analysis.\nSecond Round Response: We thank the reviewer for this valuable suggestion. We have repeated the experiment and now provide the quantification of the polysome-to-subpolysome ratio in the revised manuscript (Fig. 4h), as recommended.\nSecond Round Response: We thank the reviewer for pointing this out. The bar represents cell size. For most immunofluorescence quantifications, values were calculated using the median. We have now clarified this in the figure legend and included the number of independent experiments performed.\nSecond Round Response: We thank the reviewer for this important comment. To address whether CDK8-mediated regulation of protein synthesis is dependent on MYC, we performed additional OPP assays following MYC knockdown using shRNA and inhibition of MYC activity using Omomyc, a dominant-negative MYC peptide. In both MYC-deficient and MYC-inhibited conditions, CDK8 inhibition with RVU120 still led to a reduction in protein synthesis (Fig 5. d-f and Supplementary Fig. 6a). These results indicate that CDK8 regulates protein synthesis independently of MYC. However, cells with high MYC activity are more sensitive to CDK8 inhibition, suggesting that MYC amplifies the dependency on CDK8. These findings are now included in the revised manuscript.\nSecond Round Response: We have removed some of the gene set enrichment analysis data and substantially reorganized Figures 4 and 5 to reduce redundancy and improve clarity. Additionally, we have expanded the Discussion section and included additional references as suggested.\nSecond Round Response: Thank you for pointing this out. In the original version of Figure 1n, the IHC image was taken from a section that did not include the cerebellum, as our intention was to illustrate tumor progression at the time each group was sacrificed on the same day. To address this concern and improve anatomical clarity, we have re-sectioned the tissue to include the cerebellum and performed IHC. The updated figure now clearly shows the cerebellar structure, confirming the injection site."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I am happy with the authors' response related to normalization of sequencing data and have no futher comments.", "reviewer_2": "Thank you for the effort in responding to all my questions.The authors addressed all my concerns and I do not have additional comments.", "reviewer_3": "The authors have addressed all concerns raised during the revision process. I do not have further comments."}, "author_response": ""}]

Decision letter