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CDK10 suppresses nucleic acid sensors-mediated antitumor immunity

Nature Cancer volume 7, pages 283–303 (2026)Cite this article

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Abstract

Cancer immunotherapies have revolutionized cancer treatment, yet many patients fail to respond. Activating innate immunity offers a promising approach to enhance therapeutic efficacy, but the signaling kinases directly regulating this process to boost antitumor responses remain elusive. Here we conduct an in vivo kinome CRISPR screen and identify CDK10 as a key suppressor of tumor immune surveillance. Mechanistically, CDK10 phosphorylates DNMT1 and RAP80 to reduce the accumulation of double-stranded RNA and R-loops, which alleviates the activation of innate immune pathways mediated by MDA5 and cGAS. Kinase inhibitor screens identify NVP-AST487 and ponatinib as selective CDK10 inhibitors. Both genetic and pharmacological inhibition of CDK10 activates MDA5 and cGAS pathways, fostering an immunoactive tumor microenvironment that enhances cancer immunotherapy in multiple mouse tumor models. Clinically, low CDK10 expression in tumors correlates with better immunotherapy responses. These findings establish CDK10 as a pivotal modulator of tumor immunity and a potential therapeutic target.

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Fig. 1: CDK10 functions as a driver of cancer immune evasion.
Fig. 2: Knockout of Cdk10 shapes an immunoactive tumor microenvironment.
Fig. 3: CDK10 suppresses nucleic acid sensors-mediated innate immune responses.
Fig. 4: CDK10 phosphorylates DNMT1 at S954 to curtail dsRNA accumulation and suppress innate immune activation.
Fig. 5: By phosphorylating RAP80 at serine 379, CDK10 restricts R-loop accumulation and innate immune activation.
Fig. 6: CDK10 inhibition enhances response to immune-checkpoint blockade therapy.
Fig. 7: High-throughput screening identifies NVP-AST487 and ponatinib as CDK10 inhibitors.
Fig. 8: Ponatinib and NVP-AST487 enhance the efficacy of immune-checkpoint blockade therapy.

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Data availability

The scRNA-seq, CRISPR screening and RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession codes GSE287052, GSE287053 and GSE287054. The TMT, IP–MS proteomics and TMT phosphoproteomics data have been deposited in ProteomeXchange under primary accession code PXD059695. Data in Fig. 1e were derived from https://cancergenome.nih.gov/. Data in Figs. 1f and 6s–v and Extended Data Fig. 8p–r were derived from http://tide.dfci.harvard.edu and https://cide.ccr.cancer.gov/. Data in Fig. 6n were derived from https://cistrome.shinyapps.io/timer/. The remaining data are available within the Article, Supplementary Information and Source Data files, or available from the corresponding author on request. Source data are provided with this paper.

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Acknowledgements

We thank P. Sicinski (Dana-Farber Cancer Institute) for the discussion and advice. This work was supported by grants from the National Key Research and Development Program of China (2023YFC3402100 and 2022YFC3401500 to J.Z.), the National Natural Science Foundation of China (82273062 to J.Z.; 22193073 and 92253305 to X.L.; 82504056 to C.H.; 82503353 to H.L.; 82403052 to W.X.; 22407012 to F.G.; and 82503802 to B.C.), the Fundamental Research Funds for the Central Universities (2042022dx0003 to J.Z.), Natural Science Foundation of Wuhan (2024040701010031 to J.Z.), the open Research Fund of the National Center for Protein Sciences at Peking University in Beijing (KF-202504 to J.Z.), the Open Projects of Hubei Key Laboratory of Tumor Biological Behavior (220172107 to J.Z.), Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University (ZNJC202312 to J.Z.), Postdoctoral Fellowship Program of CPSF (GZC20250976 to C.H. and GZC20241276 to W.X.), China Postdoctoral Science Foundation (2022M722462 to H.L.), China Postdoctoral Science Foundation (2024M762491 to C.H.), Postdoctoral Project of Hubei Province (2004HBBHJD057 to C.H.; 2004HBBHCXA069 to W.X.), Beijing National Laboratory for Molecular Sciences (BNLMS- CXX-202106 to X.L.) and the New Cornerstone Science Foundation through the XPLORER PRIZE to X.L. We also thank the staff at the core facility of the Medical Research Institute at Wuhan University for their technical support. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Gaoshan Xu, Fusheng Guo, Chuan He, Xiyong Wang.

  2. These authors jointly supervised this work: Xiaoguang Lei, Jinfang Zhang.

Authors and Affiliations

  1. Department of Radiation and Medical Oncology, State Key Laboratory of Metabolism and Regulation in Complex Organisms, Medical Research Institute, Frontier Science Center of Immunology and Metabolism, Hubei Key Laboratory of Tumor Biological Behavior, Hubei Provincial Clinical Research Center for Cancer, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China

    Gaoshan Xu, Chuan He, Xiyong Wang, Bolin Xiang, Yishuang Sun, Jie Shi, Xixin Xing, Yingmeng Yao, Panpan Dai, Wenjun Xiong, Hudan Liu, Guoliang Qing, Baishan Jiang & Jinfang Zhang

  2. Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, China

    Gaoshan Xu, Chuan He, Xiyong Wang, Bolin Xiang, Yishuang Sun, Jie Shi, Xixin Xing, Yingmeng Yao, Wenjun Xiong, Hudan Liu, Guoliang Qing & Jinfang Zhang

  3. Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Fusheng Guo & Xiaoguang Lei

  4. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Fusheng Guo & Xiaoguang Lei

  5. Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China

    Lifang Fan & Yishuang Sun

  6. Department of Colorectal and Anal Surgery, Hubei Key Laboratory of Intestinal and Colorectal Diseases, Clinical Center of Intestinal and Colorectal Diseases of Hubei Province, Zhongnan Hospital of Wuhan University, Wuhan, China

    Baoxiang Chen & Congqing Jiang

  7. Department of Hematology, Medical Research Institute, Frontier Science Center for Immunology and Metabolism, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China

    Jiakun Peng & Rui Xiao

  8. Department of Dermatology, Zhongnan Hospital of Wuhan University, Wuhan, China

    Haiou Li

  9. Institute for Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China

    Xiaoguang Lei

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  1. Gaoshan Xu
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  2. Fusheng Guo
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Contributions

J.Z., G.X., X.L. and F.G. conceived of and designed the study. G.X., F.G., C.H. and X.W. conducted most of the experiments, analyzed the data and prepared the figures with help from B.X., L.F., B.C., J.P., Y.S., J.S., X.X., Y.Y., P.D., H.L. and W.X. J.P. and R.X. analyzed RNA sequencing data. L.F., B.C. and C.J. helped analyze the clinical data. F.G., B.J. and X.L. helped with high-throughput kinase library screening; J.Z. and X.L. obtained funding and supervised the study. J.Z., X.L., G.X. and F.G. wrote the manuscript. H.L. and G.Q. provided the intellectual support and discussed the research. All authors commented on the manuscript.

Corresponding authors

Correspondence to Xiaoguang Lei or Jinfang Zhang.

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Nature Cancer thanks Daniel Schramek and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 CRISPR screen identifies CDK10 as a driver of cancer immune evasion.

a–c, The sgRNA coverage (a), gini index (b), and pearson correlation (c) in cells and tumors from immunocompetent BALB/c WT and immunodeficient BALB/c nude mice. d–f, Cell Counting Kit-8 (CCK8) assay (d) and colony formation assay (e, f) for sgControl and sgCdk10 CT26 cells. g, h, Tumor growth curve (g) and end point tumor weight (h) of sgControl or sgCdk10 CT26 tumors in immunodeficient NSG mice. n = 6 mice per group. i–k, CCK8 assay (i) and colony formation assay (j, k) for sgControl and sgCdk10 MC38 cells. l, Immunoblot (IB) analysis of whole cell lysates (WCL) derived from sgControl and sgCdk10 B16F10 cells. m–o, CCK8 assay (m) and colony formation assay (n, o) for sgControl and sgCdk10 B16F10 cells. p–s, Tumor growth curve and end point tumor weight of sgControl and sgCdk10 B16F10 tumors in immunodeficient C57BL/6J Rag1−/− mice (p, q) or immunocompetent C57BL/6J WT mice (r, s). n = 6 mice for sgControl group and n = 7 mice for sgCdk10 group in C57BL/6J Rag1−/− mice, n = 10 mice for sgControl group and n = 12 mice for sgCdk10 group in C57BL/6J WT mice. t, IB analysis of WCL derived from CT26 cells with indicated treatments. u, v, Tumor growth curve (u) and end point tumor weight (v) of indicated CT26 tumors in immunocompetent BALB/c WT mice. n = 8 mice for sgControl group and sgCdk10 group, n = 9 mice for sgCdk10 group + CDK10 WT group and sgCdk10 group + CDK10 D181A group. Data are presented as mean ± SEM. For d, g, i, m, p, r, and u, analyzed by two-way ANOVA with Tukey’s multiple-comparisons test; for f, h, k, o, q, s, and v, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For d–f, i–k, m–o, n = 3 biologically independent samples per group, the graph is representative of three independent experiments with similar results. For l, t, three independent experiments were performed for each image with similar results.

Source data

Extended Data Fig. 2 Clustering analysis of various cell populations in sgControl and sgCdk10 CT26 tumors.

a, UMAP of total cells from sgControl and sgCdk10 CT26 tumors in immunocompetent BALB/c WT mice, colored by cluster. b–g, Feature plots displaying the expression of conventional marker genes across different cell types: Mcm6 (b), Mcm7 (c), Cd3g (d), Itgam (e), Col1a1 (f), and Hbb-bs (g). h, UMAP of lymphoid cells from sgControl and sgCdk10 CT26 tumors in immunocompetent BALB/c WT mice, colored by cluster. i–o, Feature plots showing the expression of lymphoid-specific marker genes across different cell types: Tcf7 (i), Cd8a (j), Ncr1 (k), Foxp3 (l), Cd79a (m), Sirpa (n) and Cd40 (o). p, The representative fluorescence-activated cell sorting (FACS) gating strategy for analyzing tumor-infiltrating immune cells was designed to comprehensively assess the leukocyte population. Gating commenced with two sequential steps to exclude doublets, followed by the selection of live cells. For immune cell analysis, a size gate and subsequent CD45 gating were applied to identify the following populations: CD8+ T cells (CD45+CD3+CD8+, as shown in Figs. 2g, q, 6d, 8e,m and Extended Data Fig. 2w,x), CD4+ T cells (CD45+CD3+CD4+, as shown in Extended Data Fig. 2q, u, v), NK cells (CD45+ CD3−NK1.1+, as shown in Fig. 2k,t), MDSC (CD45+CD11b+Gr1+, as shown in Extended Data Fig. 3n), and DCs (CD45+CD11b−CD11c+MHCⅡ+, as shown in Extended Data Fig. 3o). CD8⁺ T cells and CD4+ T cells were further assessed for frequency of IFNγ+, TNF+, and GZMB+, as shown in Figs. 2h–j, 6e–g, 8f–h, n–p and Extended Data Fig. 2r–t. NK cells, natural killer cells; MDSC, Myeloid-derived suppressor cells; DCs, Dendritic cells. q, The number of CD4+ T cells normalized to tumor weight per gram in sgControl and sgCdk10 CT26 tumors. n = 5 mice/group. r–t, The percentage of IFNγ+ (r), TNF+ (s), and GZMB+ (t) in CD4+ T cells from sgControl and sgCdk10 CT26 tumors. n = 5 mice/group. IFNγ+/CD4+ (%), TNF+/CD4+ (%), GZMB+/CD4+ (%), represent the percentages of IFNγ+, TNF+, and GZMB+ cells among total CD4+ T cells within the tumor tissue, respectively. u, v, The percentage of CD4+ T cells in CD3+ T cells (u) and the number of CD4+ T cells (v) in draining lymph nodes (dLNs) of sgControl and sgCdk10 CT26 tumors. n = 5 mice/group. w, x, The percentage of CD8+ T cells in CD3+ T cells (w) and the number of CD8+ T cells (x) in dLNs of sgControl and sgCdk10 CT26 tumors. n = 5 mice/group. CD4+/CD3+ (%) represent the percentages of CD4+ cells among total CD3+ T cells. For a–o, n = 2 biologically independent samples per group, with each sample comprising a pool of three tumors. For q–x, data are presented as mean ± SEM, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test.

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Extended Data Fig. 3 Analysis of the tumor-infiltrating myeloid cells from sgControl and sgCdk10 CT26 tumors.

a, UMAP of myeloid cells from sgControl and sgCdk10 CT26 tumors in immunocompetent BALB/c WT mice, colored by cluster. b, Dot plots showing expression patterns of conventional myeloid cell marker genes across different myeloid cell types in subcutaneous sgControl and sgCdk10 CT26 tumors from immunocompetent BALB/c WT mice. TAM, Tumor-associated macrophage. c–h, Feature plots displaying the expression of conventional myeloid marker genes across different cell types: C1qa (c), Spp1 (d), Plac8 (e), Vcan (f), H2-Aa (g), and S100a8 (h). i, j, UMAP of seven distinct myeloid cell types (i) and the percentage of each myeloid cell type (j) in sgControl and sgCdk10 CT26 tumors from immunocompetent BALB/c mice. k–m, Representative IHC images (k) and quantification (l, m) of C1qc and Spp1 from sgControl and sgCdk10 CT26 tumors in immunocompetent BALB/c WT mice. Scale bar, 50 μm. n = 5 mice/group. n, o, Flow cytometry analysis showing the percentage of MDSC+ (n) and DC+ (o) cells in CD45+ cells from sgControl and sgCdk10 CT26 tumors in immunocompetent BALB/c mice. n = 5 mice/group. p, Representative multiplex IHC (mIHC) images showing tumor cells (GFP, green), macrophages (F4/80, red), and nuclei (DAPI, blue) from GFP-overexpressing sgControl and sgCdk10 CT26 tumors. Yellow signals (indicated by arrows), representing the colocalization of GFP (green) and F4/80 (red), indicate macrophage-mediated phagocytosis of tumor cells. Scale bar: 50 μm. q, Quantification of yellow colocalized cells was performed in high-power fields. Each data point represents the average number of double-positive cells across three high-power fields. n = 4 mice/group. For a–j, n = 2 biologically independent samples per group, with each sample comprising a pool of three tumors. Data are presented as mean ± SEM. For l, m, and q, analyzed by two-tailed unpaired t-test, for n, o, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test.

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Extended Data Fig. 4 Loss of Cdk10 triggers innate immune activation.

a, b, Heatmap displaying differentially expressed genes in sgControl and sgCdk10 CT26 cells, based on transcriptomic (a) and proteomic (b) sequencing data. c, Reverse transcription quantitative PCR (RT-qPCR) analysis of the indicated genes in sgControl and sgCdk10 B16F10 cells. d, IB analysis of WCL derived from sgControl and sgCdk10 B16F10 cells. e, IB analysis of WCL derived from sgControl and sgCdk10 NCM460 cells. f, RT-qPCR analysis of the indicated genes in sgControl and sgCdk10 NCM460 cells. g, Western blot analysis of CDK10 in normal colon cells (NCM460) and various CRC cell lines. Vinculin served as a loading control. h, IB analysis of WCL derived from CT26 cells with indicated treatments. i, RT-qPCR analysis of the indicated genes. j, IB of WCL from human colorectal cancer (CRC) tumor samples, n=11 tumor samples. k–u, Correlations between the CDK10 expression and pS172-TBK1 (k), pS396-IRF3 (l), IFIT3 (m), OAS2 (n), BST2 (o), IRGM (p), GBP2 (q), OAS1 (r), STAT1 (s), IFI44 (t) and ISG15 (u) in colorectal tumor samples, n = 11 tumor samples. Data are presented as mean ± SEM, for c, f, and i, n = 3 biologically independent samples, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For k–u, correlation coefficients were calculated using the Pearson two-taided test. For d, e, g, and h, three independent experiments were performed for each image with similar results.

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Extended Data Fig. 5 Cdk10 suppresses nucleic acid sensors-mediated innate immune responses.

a, IB analysis of WCL derived from CT26 cells with indicated sgRNA treatments. b, c, pS172-TBK1 level (b) and pS396-IRF3 level (c) was quantified by ImageJ for a, which was normalized to vinculin. d, IB analysis of WCL derived from CT26 cells with indicated sgRNA treatments. e, f, pS172-TBK1 level (e) and pS396-IRF3 level (f) was quantified by ImageJ for d, which was normalized to vinculin. g, RT-qPCR analysis of the indicated genes. h, i, IB analysis of WCL derived from sgControl and sgCdk10 CT26 cells treated with Ru.521 (10 µM, 36 h) (h) or H-151 (1 µM, 36 h) (i). j, RT-qPCR analysis of the indicated genes derived from sgControl and sgCdk10 CT26 cells treated with Ru.521 (10 µM, 36 h) or H-151 (1 µM, 36 h). k, IB analysis of WCL derived from CT26 cells with indicated sgRNA treatments. l, RT-qPCR analysis of the indicated genes. m, n, Tumor growth curve (m) and end point tumor weight (n) of indicated CT26 tumors in immunocompetent BALB/c WT mice. n = 7 mice for sgControl group and sgCdk10 group, n = 6 mice for sgCdk10 + sgcGas group, sgCdk10 + sgMda5 group and sgCdk10 + sgcGas + sgMda5 group. For g, j, and l, n = 3 biologically independent samples. Data are presented as mean ± SEM, for b, c, e, f, g, j, l, and n, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test; for m, analyzed by two-way ANOVA with Tukey’s multiple-comparisons test. For a, d, h, i, and k, three independent experiments were performed for each image with similar results.

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Extended Data Fig. 6 CDK10 interacts with and phosphorylates DNMT1, thereby suppressing dsRNA production.

a, Heatmap showing differentially expressed genes in sgControl and sgCdk10 CT26 cells based on phosphoproteomics sequencing. b, RT-qPCR analysis of Dnmt1 mRNA levels in sgControl and sgCdk10 CT26 cells. c, IB analysis of WCL and anti-DNMT1 immunoprecipitation (IP) from DLD1 cells. d, IB analysis of glutathione S-transferase (GST) pull-down assays from WCL of HEK293T cells expressing Flag-DNMT1, incubated with bacterially purified recombinant GST or GST-CDK10 protein. e, Schematic illustration of DNMT1, showing its various domains and truncated mutants. f, g, Immunofluorescence (IF) images showing anti-dsRNA (J2) staining and quantification of cytoplasmic dsRNA mean fluorescence intensity (MFI) in sgControl and sgCDK10 DLD1 cells. Scale bar, 20 μm. n = 20 cell/group. h, RT-qPCR analysis of the indicated genes in sgControl and sgCdk10 CT26 cells. i, IB analysis of WCL derived from CT26 cells with indicated treatments. For b and h, n = 3 biologically independent samples, data are represented as mean ± SEM, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For g, the box represents the interquartile range (IQR); the central line represents the median, while the upper and lower edges of the box correspond to the first and third quartiles, respectively; the whiskers extend to the minimum and maximum values; all individual data points are shown, analyzed by two-tailed unpaired t-test. For c, d, f, and i, three independent experiments were performed for each image with similar results.

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Extended Data Fig. 7 CDK10 interacts with and phosphorylates RAP80 at serine 379, thereby suppressing R-loop production.

a, IB analysis of GST pull-down assays from WCL of HEK293T cells expressing HA-CDK10, incubated with bacterially purified recombinant GST or GST-hRAP80 protein. b, A schematic presentation of putative CDK10 phosphorylation motif around S379 site in RAP80. c, in vitro kinase assays using bacterially purified recombinant GST-mRap80-WT and S379A mutant proteins, incubated with or without active CDK10/cyclin M kinase. d, IB analysis of WCL and anti-HA IPs from HEK293T cells transfected with indicted mRap80 WT or S379A mutant. e, f, Representative IF images of γH2AX (red) and corresponding foci quantification. Images show γH2AX staining in sgControl and sgCdk10 CT26 cells (e). Scale bar, 20 µm. The graph quantifies the number of γH2AX foci per nucleus (f); n = 30 cells/group. g, h, IF images of S9.6 staining and quantification of nuclear and cytoplasmic R-loop (MFI) in sgControl and sgCDK10 DLD1 cells. Scale bars, 20 µm. n = 20 cell/group. i, j, IF images of GFP-dRNASEH1 staining and quantification of nuclear R-loop and cytoplasmic hybrid MFI in sgControl and sgCdk10 CT26 cells with or without treatment of RNase H (37 °C, 4 h). Scale bar, 20 μm. n = 20 cell/group. k, l, IF images of GFP-dRNASEH1 staining and quantification of nuclear R-loop and cytoplasmic hybrid MFI in indicated cells. Scale bar, 20 μm. n = 20 cell/group. For f, h, j, and l, the box represents the IQR; the central line represents the median, while the upper and lower edges of the box correspond to the first and third quartiles, respectively; the whiskers extend to the minimum and maximum values; all individual data points are shown, for f, h, analyzed by two-tailed unpaired t-test; for j, l,analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For a, c–e, g, i, and k, three independent experiments were performed for each image with similar results.

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Extended Data Fig. 8 Phenotypes of mice with Cdk10 conditional knockout (cKO) in intestinal epithelial cells.

a, Schematic diagram illustrating the cKO strategy for Cdk10 in intestinal epithelial cells of C57BL/6J mice, achieved using CRISPR/Cas9 technology to edit the Cdk10 gene. b, IB of WCL from intestinal epithelial cells (IECs) sorted from WT and Cdk10 cKO mice. n = 3 mice/group. c, d, Mendelian inheritance ratios from the interbreeding of Cdk10fl/+ and Cdk10fl/+; Vil-Cre/+ mice (c). Number and sex ratio of mice were analyzed (d). e, Body weight of female and male 8-week-old WT and Cdk10 cKO mice. For males, n = 5 mice/group; For females n = 7 mice/group. f, General appearance of representative 8-week-old WT and Cdk10 cKO mice. n = 3 mice/group. g, Representative images of the kidneys and spleens from 8-week-old WT and Cdk10 cKO mice. n = 3 mice/group. h, IB of WCL from IECs sorted from WT and Cdk10 cKO mice. n = 3 mice/group. i, RT-qPCR analysis of mRNA levels of indicated genes in IECs derived from WT and Cdk10 cKO mice. n = 3 mice/group. j, Schematic of the AOM/DSS-induced colorectal cancer model. k–m, Representative images of tumor-bearing colon and rectum (k), tumor count per mouse (l), and representative H&E staining (m) of colonic tissues from WT and Cdk10 cKO mice after AOM/DSS treatment. Tumor count was assessed at the study end point. Scale bars, 1 mm (m). n = 6 mice/group (l). n, IB of WCL from colorectal tumors sorted from WT and Cdk10 cKO mice. n = 3 mice/group. o, RT-qPCR analysis of mRNA levels of indicated genes in colorectal tumors derived from WT and Cdk10 cKO mice. n = 3 mice/group. p–r, Clinical studies showing that high CDK10 expression is associated with poor survival in patients treated with anti-PD-1 (p), anti-PD-L1 (q), and anti-CTLA-4 (r) therapy. A positive Z-value indicates that higher CDK10 expression is associated with worse survival, whereas a negative value suggests a protective effect. For e, i, l, and o, data are presented as mean ± SEM, analyzed by two-tailed unpaired t-test. For p–r, a Cox proportional hazards (CoxPH) model was applied to compute a P value reflecting the strength of the correlation between gene expression and OS.

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Extended Data Fig. 9 Ponatinib and NVP-AST487 induce CDK10-dependent activation of innate immune signaling.

a, Chemical structures of NVP-AST487 and ponatinib. b, Relative activity of CDK10/cyclin M, CDK2/cyclin E1, CDK4/cyclin D1 and CDK6/cyclin D1 following treatment with ponatinib and NVP-AST487, assessed by the NanoBret assay. c, d, IB analysis of WCL derived from sgControl and sgCdk10 CT26 cells treated with ponatinib (c) or NVP-AST487 (d) for 48 h. e, IF images showing anti-dsRNA (J2) staining and quantification of cytoplasmic dsRNA signal intensity in sgControl and sgCdk10 CT26 cells treated with ponatinib (0.25 µM, 24 h), NVP-AST487 (0.25 µM, 24 h), or DMSO. Scale bars, 20 µm. n = 20 cell/group. f, IF images of GFP-dRNASEH1 staining and quantification of nuclear R-loops and cytoplasmic hybrids in sgControl and sgCdk10 CT26 cells treated with ponatinib (0.25 µM, 24 h), NVP-AST487 (0.25 µM, 24 h), or DMSO. Scale bars, 20 µm. n = 20 cell/group. g, h, mRNA expression levels of the indicated genes in sgControl and sgCdk10 CT26 cells treated with ponatinib (0.25 μM, 48 h) (g), NVP-AST487 (0.25 µM, 48 h) (h) or DMSO. For e, f, the box represents the IQR; the central line represents the median, while the upper and lower edges of the box correspond to the first and third quartiles, respectively; the whiskers extend to the minimum and maximum values, all individual data points are shown, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For g, h, n = 3 biologically independent samples, data are presented as mean ± SEM, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For b–f, three independent experiments were performed with similar results, and a representative experiment is shown.

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Extended Data Fig. 10 Ponatinib and NVP-AST487 exhibit no notable toxicity in mice and demonstrate superior antitumor efficacy compared to palbociclib in the CT26 murine model.

a, b, Tumor growth curve (a) and end point tumor weight (b) of sgControl and sgCdk10 CT26 tumors in immunodeficient BALB/c nude mice, treated with ponatinib (30 mg/kg), NVP-AST487 (30 mg/kg) or Vehicle. n = 9 mice for sgControl + Vehicle group, sgControl + ponatinib group and sgControl + NVP-AST487 group, n = 10 mice/group for sgCdk10 + Vehicle group, sgCdk10 + ponatinib group and sgCdk10 + NVP-AST487 group. c, Time-course monitoring of body weight in immunocompetent BALB/c WT mice during the indicated treatment. n = 7 mice/group. d–f, RBC (d), WBC (e), and PLT (f) counts were measured in immunocompetent BALB/c WT mice with the indicated treatments. n = 7 mice/group. g, h, Liver function was evaluated by measuring AST (g) and ALT (h) levels in immunocompetent BALB/c WT mice following the indicated treatments. n = 7 mice/group. i, H&E staining for the slices of the several organs (liver, spleen, lung and colon) from immunocompetent BALB/c WT mice. Scale bar, 50 μm. j, k, Growth curve (j) and Kaplan–Meier (k) survival curves of subcutaneous WT CT26 tumors in immunocompetent BALB/c WT mice following the indicated treatments. n = 12 mice/group. Anti-PD-1 antibody treatment was given by i.p. injection of 200 µg per mouse every three days, for a total of seven injections. l, A schematic model of CDK10-mediated regulation of tumor immunity. Schematic model of CDK10-mediated regulation of tumor immunity. CDK10 plays a critical role in suppressing the accumulation of dsRNA and R-loops by phosphorylating DNMT1 and RAP80. This phosphorylation prevents the activation of the MDA5 and cGAS-STING innate immune pathways. Consequently, genetic or pharmacological inhibition of CDK10 triggers the activation of both the MDA5 and cGAS-STING pathways, thereby enhancing antitumor immunity and improving the efficacy of immune-checkpoint blockade (ICB) therapy. Data are represented as mean ± SEM, for a, c and j, analyzed by two-way ANOVA with Tukey’s multiple-comparisons test; for b, d–h, analyzed by one-way ANOVA with Tukey’s multiple-comparisons test. For k, Kaplan–Meier survival analysis and two-tailed Gehan–Breslow-Wilcoxon test were performed.

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Xu, G., Guo, F., He, C. et al. CDK10 suppresses nucleic acid sensors-mediated antitumor immunity. Nat Cancer 7, 283–303 (2026). https://doi.org/10.1038/s43018-025-01100-3

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