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CPSF3-dependent pre-mRNA processing as a druggable node in AML and Ewing’s sarcoma

An Author Correction to this article was published on 04 March 2020

This article has been updated


The post-genomic era has seen many advances inBaryza and G. Rice for helpful our understanding of cancer pathways, yet resistance and tumor heterogeneity necessitate multiple approaches to target even monogenic tumors. Here, we combine phenotypic screening with chemical genetics to identify pre-messenger RNA endonuclease cleavage and polyadenylation specificity factor 3 (CPSF3) as the target of JTE-607, a small molecule with previously unknown target. We show that CPSF3 represents a synthetic lethal node in a subset of acute myeloid leukemia (AML) and Ewing’s sarcoma cancer cell lines. Inhibition of CPSF3 by JTE-607 alters expression of known downstream effectors in AML and Ewing’s sarcoma lines, upregulates apoptosis and causes tumor-selective stasis in mouse xenografts. Mechanistically, it prevents the release of newly synthesized pre-mRNAs, resulting in read-through transcription and the formation of DNA-RNA hybrid R-loop structures. This study implicates pre-mRNA processing, and specifically CPSF3, as a druggable target providing an avenue to therapeutic intervention in cancer.

Fig. 1: In vitro growth of AML and Ewing’s sarcoma cancer cell lines is sensitive to compound 1.
Fig. 2: Chemical genetics and proteomics studies combine to identify mRNA processing and CPSF3 as the target of compound 2.
Fig. 3: Compound 2 binds directly to CPSF3, inhibiting its endonuclease activity and conferring growth inhibitory effects.
Fig. 4: Compound 1 induces transcript accumulation and RNA Pol II read-through.
Fig. 5: Compound 1 results in accumulation of nuclear R-loops.
Fig. 6: Proposed model for compound 2 MoA.

Data availability

The datasets generated during and/or analyzed during the current study are included in this published article (and its Supplementary Information files). The raw RNA-sequencing reads are available in the NCBI Sequence Read Archive under accession number SRP158650. X-ray structure data have been deposited to the PDB with the code 6M8Q. All other relevant data are available from the corresponding author on reasonable request.

Change history

  • 04 March 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • 02 April 2020

    The article was updated on 04 March 2020.


  1. 1.

    Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    CAS  PubMed  Google Scholar 

  2. 2.

    Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. & Prunotto, M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).

    CAS  PubMed  Google Scholar 

  3. 3.

    Schreiber, S. L. Chemical genetics resulting from a passion for synthetic organic chemistry. Bioorg. Med. Chem. 6, 1127–1152 (1998).

    CAS  PubMed  Google Scholar 

  4. 4.

    Carson, C. et al. Englerin A agonizes the TRPC4/C5 cation channels to inhibit tumor cell line proliferation. PLoS ONE 10, e0127498 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Rothman, D. M. et al. Metabolic enzyme sulfotransferase 1A1 is the trigger for N-benzyl indole carbinol tumor growth suppression. Chem. Biol. 22, 1228–1237 (2015).

    CAS  PubMed  Google Scholar 

  6. 6.

    Kakutani, M., Takeuchi, K., Waga, I., Iwamura, H. & Wakitani, K. JTE-607, a novel inflammatory cytokine synthesis inhibitor without immunosuppression, protects from endotoxin shock in mice. Inflamm. Res. 48, 461–468 (1999).

    CAS  PubMed  Google Scholar 

  7. 7.

    Borozdenkova, S. et al. Effects of a cytokine inhibitor, JTE-607, on the response to endotoxin in healthy human volunteers. Int. Immunopharmacol. 11, 1837–1843 (2011).

    CAS  PubMed  Google Scholar 

  8. 8.

    Ryugo, M. et al. Pharmacologic preconditioning of JTE-607, a novel cytokine inhibitor, attenuates ischemia-reperfusion injury in the myocardium. J. Thorac. Cardiovasc. Surg. 127, 1723–1727 (2004).

    CAS  PubMed  Google Scholar 

  9. 9.

    Jian, M. Y., Koizumi, T., Tsushima, K. & Kubo, K. JTE-607, a cytokine release blocker, attenuates acid aspiration-induced lung injury in rats. Eur. J. Pharmacol. 488, 231–238 (2004).

    CAS  PubMed  Google Scholar 

  10. 10.

    Uesato, N., Fukui, K., Maruhashi, J., Tojo, A. & Tajima, N. JTE-607, a multiple cytokine production inhibitor, ameliorates disease in a SCID mouse xenograft acute myeloid leukemia model. Exp. Hematol. 34, 1385–1392 (2006).

    CAS  PubMed  Google Scholar 

  11. 11.

    Tajima, N. et al. JTE-607, a multiple cytokine production inhibitor, induces apoptosis accompanied by an increase in p21waf1/cip1 in acute myelogenous leukemia cells. Cancer Sci. 101, 774–781 (2010).

    CAS  PubMed  Google Scholar 

  12. 12.

    Li, B. E. & Ernst, P. Two decades of leukemia oncoprotein epistasis: the MLL1 paradigm for epigenetic deregulation in leukemia. Exp. Hematol. 42, 995–1012 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lessnick, S. L. & Ladanyi, M. Molecular pathogenesis of Ewing sarcoma: new therapeutic and transcriptional targets. Annu. Rev. Pathol. 7, 145–159 (2012).

    CAS  PubMed  Google Scholar 

  14. 14.

    Kim, N. & Jinks-Robertson, S. Transcription as a source of genome instability. Nat. Rev. Genet. 13, 204–214 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Danckwardt, S., Hentze, M. W. & Kulozik, A. E. 3′ end mRNA processing: molecular mechanisms and implications for health and disease. EMBO J. 27, 482–498 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Somervaille, T. C. P. et al. Hierarchical maintenance of MLL myeloid leukemia stem cells employs a transcriptional program shared with embryonic rather than adult stem cells. Cell Stem Cell 4, 129–140 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Zuber, J. et al. An integrated approach to dissecting oncogene addiction implicates a Myb-coordinated self-renewal program as essential for leukemia maintenance. Genes Dev. 25, 1628–1640 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Smith, R. et al. Expression profiling of EWS/FLI identifies NKX2.2 as a critical target gene in Ewing’s sarcoma. Cancer Cell 9, 405–416 (2006).

    CAS  PubMed  Google Scholar 

  20. 20.

    Tirode, F. et al. Mesenchymal stem cell features of Ewing tumors. Cancer Cell 11, 421–429 (2007).

    CAS  PubMed  Google Scholar 

  21. 21.

    Schirle, M. & Jenkins, J. L. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov. Today 21, 82–89 (2016).

    CAS  PubMed  Google Scholar 

  22. 22.

    MacKeigan, J. P., Murphy, L. O. & Blenis, J. Sensitized RNAi screen of human kinases and phosphatases identifies new regulators of apoptosis and chemoresistance. Nat. Cell Biol. 7, 591–600 (2005).

    CAS  PubMed  Google Scholar 

  23. 23.

    König, R. et al. A probability-based approach for the analysis of large-scale RNAi screens. Nat. Methods 4, 847–849 (2007).

    PubMed  Google Scholar 

  24. 24.

    Zuberi, K. et al. GeneMANIA prediction server 2013 update. Nucleic Acids Res. 41, W115–W122 (2013).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Gower, C. M. et al. Conversion of a single polypharmacological agent into selective bivalent inhibitors of intracellular kinase activity. ACS Chem. Biol. 11, 121–131 (2016).

    CAS  PubMed  Google Scholar 

  26. 26.

    Thomas, J. R. et al. in Proteomics for Drug Discovery: Methods and Protocols (eds. Lazar, I. M. et al.) 1–18 (Springer, 2017).

  27. 27.

    Salcius, M. et al. SEC–TID: a label-free method for small-molecule target identification. J. Biomol. Screen. 19, 917–927 (2014).

    PubMed  Google Scholar 

  28. 28.

    Mandel, C. R. et al. Polyadenylation factor CPSF-73 is the pre-mRNA 3′-end-processing endonuclease. Nature 444, 953–956 (2006).

    CAS  PubMed  Google Scholar 

  29. 29.

    Hill, C. H. et al. Activation of the endonuclease that defines mRNA 3′ ends requires incorporation into an 8-subunit core cleavage and polyadenylation factor complex. Mol. Cell 73, 1217–1231 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Bill, A. et al. Variomics screen identifies the re-entrant loop of the calcium-activated chloride channel ANO1 that facilitates channel activation. J. Biol. Chem. 290, 889–903 (2015).

    CAS  PubMed  Google Scholar 

  31. 31.

    Huang, Z. et al. A functional variomics tool for discovering drug-resistance genes and drug targets. Cell Rep. 3, 577–585 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Grohar, P. J. et al. Functional genomic screening reveals splicing of the EWS-FLI1 fusion transcript as a vulnerability in Ewing sarcoma. Cell Rep. 14, 598–610 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Riggi, N. et al. EWS-FLI1 utilizes divergent chromatin remodeling mechansims to directly activate or repress enhancer elements in Ewing sarcoma. Cancer Cell 26, 668–681 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Tomazou, E. M. et al. Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion proteins EWS-FLI1. Cell Rep. 10, 1082–1095 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Zhao, L. et al. Integrated genome-wide chromatin occupancy and expression analyses identify key myeloid pro-differentiation transcription factors repressed by Myb. Nucleic Acids Res. 39, 4664–4679 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kerry, J. et al. MLL-AF4 spreading identifies binding sites that are distinct from super-enhancers and that govern sensitivity to DOT1L inhibition in leukemia. Cell Rep. 18, 482–495 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Gorthi, A. et al. EWS-FLI1 increases transcription to cause R-Loops and block BRCA1 repair in Ewing sarcoma. Nature 555, 387–391 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Stirling, P. C. et al. R-loop-mediated genome instability in mRNA cleavage and polyadenylation mutants. Genes Dev. 26, 163–175 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Kakegawa, J., Sakane, N., Suzuki, K. & Yoshida, T. JTE-607, a multiple cytokine production inhibitor, targets CPSF3 and inhibits pre-mRNA processing. Biochem. Biophys. Res. Commun. 518, 32–37 (2019).

    CAS  PubMed  Google Scholar 

  40. 40.

    Iniguez, A. B. et al. EWS/FLI confers tumor cell synthetic lethality to CDK12 inhibition in Ewing sarcoma. Cancer Cell 33, 202–216 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Fidaleo, M. et al. Genotoxic stress inhibits Ewing sarcoma cell growth by modulating alternative pre-mRNA processing of the RNA helicase DHX9. Oncotarget 6, 31740–31757 (2015).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Selvanathan, S. P. et al. Oncogenic fusion protein EWS-FLI1 is a network hub that regulates alternative splicing. Proc. Natl Acad. Sci. USA 112, E1307–E1316 (2015).

    CAS  PubMed  Google Scholar 

  43. 43.

    Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Dubbury, S. J., Boutz, P. L. & Sharp, P. A. CDK12 regulates DNA repair genes by suppressing intronic polyadenylation. Nature 564, 141–145 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Teloni, F. et al. Efficient pre-mRNA cleavage prevents replication-stress-associated genome instability. Mol. Cell 73, 670–683 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Kaida, D. et al. Spliceostatin A targets SF3b and inhibits both splicing and nuclear retention of pre-mRNA. Nat. Chem. Biol. 3, 576–583 (2007).

    CAS  PubMed  Google Scholar 

  47. 47.

    Palacino, J. et al. SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat. Chem. Biol. 11, 511–517 (2015).

    CAS  PubMed  Google Scholar 

  48. 48.

    Sonoiki, E. et al. A potent antimalarial benzoxaborole targets a Plasmodium falciparum cleavage and polyadenylation specificity factor homologue. Nat. Commun. 8, 1–11 (2017).

    Google Scholar 

  49. 49.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Vonrhein, C. et al. Data processing and analysis with the autoPROC toolbox. Acta Crystallogr. D. 67, 293–302 (2011).

    CAS  PubMed  Google Scholar 

  51. 51.

    McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D. 66, 486–501 (2010).

    CAS  Google Scholar 

  53. 53.

    Smart, O. S. et al. Exploiting structure similarity in refinement: automated NCS and target-structure restraints in BUSTER. Acta Crystallogr. D. 68, 368–380 (2012).

    CAS  PubMed  Google Scholar 

  54. 54.

    Meerbrey, K. L. et al. The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo. Proc. Natl Acad. Sci. USA 108, 3665–3670 (2011).

    CAS  PubMed  Google Scholar 

  55. 55.

    Lionnet, T. et al. A transgenic mouse for in vivo detection of endogenous labeled mRNA. Nat. Methods 8, 165–170 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).

    CAS  Google Scholar 

  57. 57.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  58. 58.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

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We thank H. Großhans, T.S. Miki, D. Porter, R. Tiedt, J. Baryza and G. Rice for helpful discussions through the course of this research study. This work was supported by the Novartis Research Foundation (J.A.C.), the SNF-NCCR RNA & Disease network (J.A.C.) and the Medical Research Council (MRC) grant no. MC_U105192715 (L.A.P.).

Author information




R.E.J.B., F.L. and N.T.R conceptualized research, interpreted data and wrote the manuscript. S.C. conducted and interpreted biomarker, variomics and MoA studies. A.F. synthesized all compounds. W.A.W. conducted crystallography experiments and solved structures. M.H. conducted the siRNA synergy experiment. M.S. and G.M. conducted and interpreted CPSF3 biochemical binding studies. J.T., S.C. and F.A.A. prepared RNA samples. J.K. and W.C. conducted RNA-sequencing experiments. S.H.C., S.G., S.S. and G.R. interpreted RNA-seq results. S.B., J.M., J.T. and M.S. conducted and interpreted chemical proteomics experiments. G.M. and S.C. conducted and interpreted FACS experiments. Y.W. and J.J. contributed to in silico target predictions. F.S. analyzed siRNA experiments. H.M. conducted cell line cytotoxicity studies. G.B. and E.G. conducted and interpreted in vivo xenograft experiments. A.C. and K.X. conducted variomics cloning experiments. J.S.R.-H. designed variomics cloning strategy. M.Z. and M.S. conducted mass spectrometry experiments. C.S. and E.T.W. conducted in vivo compound pharmacokinetics experiments. J.H.W., C.A.-R., M.J. and J.A.C. conducted and interpreted FISH and R-loop experiments. J.B.R.M. and L.A.P. conducted and interpreted in vitro cleavage experiments. J.A.C. and J.T. helped write the manuscript.

Corresponding author

Correspondence to Rohan E. J. Beckwith.

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Competing interests

All authors (except otherwise noted) were employees of Novartis Institutes for BioMedical Research at the time of their involvement in this study and may hold stock in Novartis.

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Supplementary information

Supplementary information

Supplementary Figures 1–21 and Supplementary Tables 1–4

Reporting Summary

Supplementary Note

Synthetic Procedures

Supplementary Dataset 1

siRNA library.

Supplementary Dataset 2

siRNA plus compound 1 synergy screen gene level activity per compound 1, DMSO or differential.

Supplementary Dataset 3

Chemoproteomics data.

Supplementary Dataset 4

PAL data.

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Ross, N.T., Lohmann, F., Carbonneau, S. et al. CPSF3-dependent pre-mRNA processing as a druggable node in AML and Ewing’s sarcoma. Nat Chem Biol 16, 50–59 (2020).

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