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Dissolution of oncofusion transcription factor condensates for cancer therapy

Abstract

Cancer-associated chromosomal rearrangements can result in the expression of numerous pathogenic fusion proteins. The mechanisms by which fusion proteins contribute to oncogenesis are largely unknown, and effective therapies for fusion-associated cancers are lacking. Here we comprehensively scrutinized fusion proteins found in various cancers. We found that many fusion proteins are composed of phase separation-prone domains (PSs) and DNA-binding domains (DBDs), and these fusions have strong correlations with aberrant gene expression patterns. Furthermore, we established a high-throughput screening method, named DropScan, to screen drugs capable of modulating aberrant condensates. One of the drugs identified via DropScan, LY2835219, effectively dissolved condensates in reporter cell lines expressing Ewing sarcoma fusions and partially rescued the abnormal expression of target genes. Our results indicate that aberrant phase separation is likely a common mechanism for these PS–DBD fusion-related cancers and suggest that modulating aberrant phase separation is a potential route to treat these diseases.

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Fig. 1: Identification of cancer-associated PS–DBD fusion proteins as candidates for aberrant phase separation.
Fig. 2: The FUS–ERG fusion protein drives phase separation in cellulo and in vitro.
Fig. 3: DBD domains tend to fuse PS-related regions, and such fusions are likely to drive downstream transcription.
Fig. 4: DropScan, a high-content screening method for identifying phase separation-modulating compounds.
Fig. 5: LY2835219 dissolves condensates via activation of lysosomes.
Fig. 6: LY2835219 reduces droplet numbers and rescues abnormal gene expression.

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Data availability

All sequencing data that support the findings of this study have been deposited in the National Center for Biotechnology Information GEO and are accessible through the GEO Series accession numbers GSE194374 and GSE232072. Drug screening data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

All custom scripts have been made available at https://github.com/TingtingLiGroup/DropScan.

References

  1. Michmerhuizen, N. L., Klco, J. M. & Mullighan, C. G. Mechanistic insights and potential therapeutic approaches for NUP98-rearranged hematologic malignancies. Blood 136, 2275–2289 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Groffen, J. et al. Philadelphia chromosomal breakpoints are clustered within a limited region, bcr, on chromosome-22. Cell 36, 93–99 (1984).

    Article  CAS  PubMed  Google Scholar 

  3. Mitelman, F., Johansson, B. & Mertens, F. The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 7, 233–245 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Boija, A., Klein, I. A. & Young, R. A. Biomolecular condensates and cancer. Cancer Res. 39, 174–192 (2021).

    CAS  Google Scholar 

  6. Li, P. L. et al. Phase transitions in the assembly of multivalent signalling proteins. Nature 483, 336–340 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ahn, J. H. et al. Phase separation drives aberrant chromatin looping and cancer development. Nature 595, 591–595 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361, eaar2555 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zuo, L. Y. et al. Loci-specific phase separation of FET fusion oncoproteins promotes gene transcription. Nat. Commun. 12, 1491 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Grunewald, T. G. P. et al. Ewing sarcoma. Nat. Rev. Dis. Prim. 4, 5 (2018).

    Article  PubMed  Google Scholar 

  12. Boulay, G. et al. Cancer-specific retargeting of BAF complexes by a prion-like domain. Cell 171, 163–178 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wei, M. T. et al. Nucleated transcriptional condensates amplify gene expression. Nat. Cell Biol. 22, 1187–1196 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Thomsen, C., Grundevik, P., Elias, P., Stahlberg, A. & Aman, P. A conserved N-terminal motif is required for complex formation between FUS, EWSR1, TAF15 and their oncogenic fusion proteins. FASEB J. 27, 4965–4974 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Wheeler, R. J. et al. Small molecules for modulating protein driven liquid–liquid phase separation in treating neurodegenerative disease. Preprint at bioRxiv https://doi.org/10.1101/721001 (2019).

  16. Fang, M. Y. et al. Small-molecule modulation of TDP-43 recruitment to stress granules prevents persistent TDP-43 accumulation in ALS/FTD. Neuron 103, 802–819 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Risso-Ballester, J. et al. A condensate-hardening drug blocks RSV replication in vivo. Nature 595, 596–599 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Terlecki-Zaniewicz, S. et al. Biomolecular condensation of NUP98 fusion proteins drives leukemogenic gene expression. Nat. Struct. Mol. Biol. 28, 190–201 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kim, P. & Zhou, X. FusionGDB: fusion gene annotation database. Nucleic Acids Res. 47, D994–D1004 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Jang, Y. E. et al. ChimerDB 4.0: an updated and expanded database of fusion genes. Nucleic Acids Res. 48, D817–D824 (2020).

    CAS  PubMed  Google Scholar 

  21. Mitelman, F., Johansson, B. & Mertens, F. Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer https://mitelmandatabase.isb-cgc.org/ (2022).

  22. Wootton, J. C. & Federhen, S. Statistics of local complexity in amino acid sequences and sequence databases. Comput. Chem. 17, 149–163 (1993).

    Article  CAS  Google Scholar 

  23. Lancaster, A. K., Nutter-Upham, A., Lindquist, S. & King, O. D. PLAAC: a web and command-line application to identify proteins with prion-like amino acid composition. Bioinformatics 30, 2501–2502 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Walsh, I., Martin, A. J., Di Domenico, T. & Tosatto, S. C. Espritz: accurate and fast prediction of protein disorder. Bioinformatics 28, 503–509 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Zanegina, O. et al. An updated version of NPIDB includes new classifications of DNA–protein complexes and their families. Nucleic Acids Res. 44, 144–153 (2016).

    Article  Google Scholar 

  26. Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics 11, 431 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ichikawa, H., Shimizu, K., Hayashi, Y. & Ohki, M. An RNA-binding protein gene, TLS/FUS, is fused to ERG in human myeloid-leukemia with t(16,21) chromosomal translocation. Cancer Res. 54, 2865–2868 (1994).

    CAS  PubMed  Google Scholar 

  28. Sizemore, G. M., Pitarresi, J. R., Balakrishnan, S. & Ostrowski, M. C. The ETS family of oncogenic transcription factors in solid tumours. Nat. Rev. Cancer 17, 337–351 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Kong, X. T. et al. Consistent detection of TLS/FUS–ERG chimeric transcripts in acute myeloid leukemia with t(16;21)(p11;q22) and identification of a novel transcript. Blood 90, 1192–1199 (1997).

    CAS  PubMed  Google Scholar 

  30. Cooper, C. D., Newman, J. A., Aitkenhead, H., Allerston, C. K. & Gileadi, O. Structures of the ETS protein DNA-binding domains of transcription factors ETV1, ETV4, ETV5, and FEV: determinants of DNA binding and redox regulation by disulfide bond formation. J. Biol. Chem. 290, 13692–13709 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shin, Y. et al. Spatiotemporal control of intracellular phase transitions using light-activated optodroplets. Cell 168, 159–171 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Pio, F. et al. New insights on DNA recognition by ETS proteins from the crystal structure of the PU.1 ETS domain–DNA complex. J. Biol. Chem. 271, 23329–23337 (1996).

    Article  CAS  PubMed  Google Scholar 

  33. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Morin, J. A. et al. Sequence-dependent surface condensation of a pioneer transcription factor on DNA. Nat. Phys. 18, 271–276 (2022).

    Article  CAS  Google Scholar 

  35. Patton, J. G., Porro, E. B., Galceran, J., Tempst, P. & Nadal-Ginard, B. Cloning and characterization of PSF, a novel pre-mRNA splicing factor. Genes Dev. 7, 393–406 (1993).

    Article  CAS  PubMed  Google Scholar 

  36. Mathur, M. & Samuels, H. H. Role of PSF–TFE3 oncoprotein in the development of papillary renal cell carcinomas. Oncogene 26, 277–283 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Xie, Z. et al. Gene set knowledge discovery with enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ghandi, M. et al. Next-generation characterization of the cancer cell line encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Corona, S. P. & Generali, D. Abemaciclib: a CDK4/6 inhibitor for the treatment of HR+/HER2 advanced breast cancer. Drug Des. Devel. Ther. 12, 321–330 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gelbert, L. M. et al. Preclinical characterization of the CDK4/6 inhibitor LY2835219: in-vivo cell cycle-dependent/independent anti-tumor activities alone/in combination with gemcitabine. Invest. New Drugs 32, 825–837 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hino, H. et al. Abemaciclib induces atypical cell death in cancer cells characterized by formation of cytoplasmic vacuoles derived from lysosomes. Cancer Sci. 111, 2132–2145 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Whitmarsh-Everiss, T. & Laraia, L. Small molecule probes for targeting autophagy. Nat. Chem. Biol. 17, 653–664 (2021).

    Article  CAS  PubMed  Google Scholar 

  43. Deng, Q. et al. Oncofusion-driven de novo enhancer assembly promotes malignancy in Ewing sarcoma via aberrant expression of the stereociliary protein LOXHD1. Cell Rep. 39, 110971 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  46. Zhu, G. et al. Phase separation of disease-associated SHP2 mutants underlies mapk hyperactivation. Cell 183, 490–502 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhu, G. Y. et al. Pharmacological inhibition of SRC-1 phase separation suppresses YAP oncogenic transcription activity. Cell Res. 31, 1028–1031 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Dowless, M. et al. Abemaciclib is active in preclinical models of Ewing sarcoma via multipronged regulation of cell cycle, DNA methylation, and interferon pathway signaling. Clin. Cancer Res. 24, 6028–6039 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Alberti, S. & Hyman, A. A. Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing. Nat. Rev. Mol. Cell Biol. 22, 196–213 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Malik, I., Kelley, C. P., Wang, E. T. & Todd, P. K. Molecular mechanisms underlying nucleotide repeat expansion disorders. Nat. Rev. Mol. Cell Biol. 22, 589–607 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tompa, P. Intrinsically unstructured proteins. Trends Biochem. Sci. 27, 527–533 (2002).

    Article  CAS  PubMed  Google Scholar 

  52. Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. King, O. D., Gitler, A. D. & Shorter, J. The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 1462, 61–80 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Chen, Z. et al. Screening membraneless organelle participants with machine-learning models that integrate multimodal features. Proc. Natl Acad. Sci. USA 119, e2115369119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Fang, Z., Liu, X. & Peltz, G. GSEApy: a comprehensive package for performing gene set enrichment analysis in python. Bioinformatics 39, btac757 (2022).

    Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge L. Yu and Y. Lin (Tsinghua University) and Z. Qi (Peking University) for providing plasmids as gifts. We are grateful to the Center of Pharmaceutical Technology (Tsinghua University) for high-content screening support. We are also grateful to the Nikon Biological Imaging Center (Tsinghua University) for confocal imaging support and the Center of Biomedical Analysis (Tsinghua University) for flow cytometry support. This work was supported by grants from the National Key R&D Program (2019YFA0508400 and 2019YFA0508403 to P.L., 2021YFF1200900 and 2018YFA0507504 to T.L.) and the Natural Science Foundation of China (32125010 and 32150023 to P.L. and 32070666 to T.L.).

Author information

Authors and Affiliations

Authors

Contributions

Y.W., C.Y., T.L. and P.L. conceived the ideas. T.L. and P.L. supervised the project. Y.W. performed all cell biology experiments and performed biochemical and genetic experiments with support from G.P. and W.J. C.Y. performed all bioinformatic analyses and image analyses. Y.W., C.Y. and G.P. performed data analyses. Y.W, C.Y. and G.P. wrote the paper. T.L. and P.L. edited the paper. All authors have commented on and approved the paper.

Corresponding authors

Correspondence to Tingting Li or Pilong Li.

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Competing interests

Tsinghua University and Peking University have filed a patent application on the basis of this work. The authors declare no competing interests.

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Nature Chemical Biology thanks Ultan McDermott 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 Cell experiments confirm phase separation of FUS-ERG.

a, SDS-PAGE analysis of MBP-EGFP-FUS-ERG purified from E. coli. b, Agarose electrophoresis analysis of purified 306-bp 25× GGAA dsDNA (labeled with cy5). c, Sequence of 306-bp dsDNA with 25× GGAA bases highlighted in red. d. Images of 40 μM EGFP-FUS-ERG mixed with 50 nM 306-bp dsDNA. The 306-bp dsDNA contained 25× GGAA and was labeled with Cy5. Experiments were performed three times with similar results. Scale bar, 10 μm. e, Representative fluorescence images of FRAP of the condensates formed by EGFP-FUS-ERG (40 μM) and 306-bp dsDNA (50 nM, 25× GGAA, labeled with Cy5). Scale bar, 2 μm. f, Quantitative analysis of the FRAP data in e. n = 6 independent measurements, data are presented as mean ± SD. g, Representative fluorescence images of FRAP for FUS-ERG and FUS-ERGmut in 293 T. A 546 nm laser with 10% power was used, and the photobleaching time was 1 second. Scale bar, 10 μm. h, Quantitative analysis of the FRAP data in g, n = 6 (for FUS-ERG) and n = 3 (for FUS-ERGmut) independent measurements, data are presented as mean ± SD. i, Representative images of FUS-ERGmut fusion proteins in 293 T treated with 10%(w/v) 1,6-hexanediol. Experiments were performed three times with similar results. Scale bar, 10 μm.

Source data

Extended Data Fig. 2 PS proteins drive phase separation in cellulo and in vitro.

a, SDS-PAGE analyses of indicated PS proteins purified from E. coli. b-f, PS proteins drive phase separation in vitro. (Top panel in b-f) Representative images and quantification of OptoIDR results of the indicated SFPQ IDR-mCherry-Cry2olig (b), MED15 IDR-mCherry-Cry2olig (c), COL17A1 IDR-mCherry-Cry2olig (d), BRD9 IDR-mCherry-Cry2olig (e), MLLT1 IDR-mCherry-Cry2olig (f). Scale bars, 20 µm. n = 29, 28, 32 and 32 independent cells per construct for SFPQ, MED15, MLLT1 and mCherry (negative control). n = 28, 20 and 36 independent cells per construct for COL17A1, BRD9 and mCherry (negative control). Data are presented as mean values +/- 95% confidence interval. (Middle panel in b-f) Confocal fluorescence images of EGFP-SFPQ IDR, EGFP-MED15 IDR, COL17A1 IDR (Alexa 488 labeled), BRD9 IDR (Alexa 488 labeled) and MLLT1 IDR (Alexa 488 labeled) at the indicated concentrations. Experiments were performed three times with similar results. Scale bars, 10 μm. (bottom panel in b-f) FRAP and quantitative analysis of the condensates formed by EGFP-SFPQ IDR (10 μM), EGFP-MED15 IDR (10 μM), COL17A1 IDR (100 μM), BRD9 IDR (150 μM), MLLT1 IDR (25 μM). The FRAP data are from different independent measurements: for EGFP-SFPQ IDR (n = 8), EGFP-MED15 IDR (n = 9), COL17A1 IDR (n = 6), BRD9 IDR (n = 9) and MLLT1 IDR (n = 12). Data are presented as mean ± SD. Scale bars, 2 μm.

Source data

Extended Data Fig. 3 Validations of selected PS-DBD fusion proteins.

Schematic diagram of indicated PS, DBD and PS-DBD fusion proteins and disorder prediction (left in each dotted box), the red rectangle indicates DBD. Live-cell images of HeLa cells that ectopically express indicated mCherry-PS-DBD and its mutated DNA-binding domain form mCherry-PS-DBDmut (right in each dotted box). DNA is stained by Hoechst 33342. Experiments were performed three times with similar results. Scale bars, 10 μm.

Extended Data Fig. 4 Cell experiments prove phase separation of PS-DBD fusion proteins.

a-f, Representative data of FRAP and 1,6-HD treatment assay for PS-DBD fusion proteins. SFPQ-TFE3 in a, SFPQ-TFE3mut in b, MED15-TFE in c, MED15-TFE3mut in d, EWS-FLI1 in e, EWS-FLI1mut in f. (Left panel) Representative images of FRAP for PS-DBD/PS-DBDmut fusion proteins in 293T. A 546 nm laser with 10% power was used, and the photobleaching time was 1 second. Scale bar, 10 μm. (Middle panel) Corresponding quantitative data of FRAP. (Right panel) Representative images of PS-DBD/PS-DBDmut fusion proteins in 293T treated with 10%[w/v] 1,6-hexanediol. Scale bar, 10 μm. For the FRAP in a-f, data are from different independent measurements: n = 4 for a, n = 3 for b, n = 6 for c, n = 6 for d, n = 8 for e, n = 3 for f. Data are presented as mean ± SD. For the 1,6-hexanediol results in a-f, all experiments were performed three times with similar results.

Source data

Extended Data Fig. 5 PS-DBD fusions cause aberrant target gene expression.

a, GSEA analysis of DBD targets for PS-DBD fusion tumor samples in the TCGA database. For each sample with a PS-DBD fusion, samples of the same tumor type without this fusion were selected as the background set. b, GSEA analysis of FLI1 targets for EWS-FLI1 fusion cell lines in the CCLE database. Six cell lines with EWS-FLI1 fusions which significantly increase the downstream transcription are shown; cell lines from bone without EWS-FLI1 fusion were used as background.

Extended Data Fig. 6 Representative hits compounds screened by DropScan.

a, DropScan workflow. DropScan uses U2OS/mCherry-FUS-ERGmut cells to screen small molecules from the APExBio Anti-cancer compound library Plus. The system monitors the fraction of cells with droplets in a time-series manner. (b-i), Characteristics of 8 representative hit compounds.Corresponding images and quantifications before and after 6 hours treatment are shown for CHIR-124 (b), GDC-0941 (c), EMD-1214063 (d), AZD-9291 (e), CX-6258 (f), UNC 0631 (g), Pelitinib (h) and Ursolic acid (i). Scale bar, 50 μm.

Source data

Extended Data Fig. 7 LY2835219 decreases the amounts of condensate and activates lysosome.

a, Live-cell images of U2OS/mCherry-FUS-ERGmut cells treated by LY2835219 with indicated concentration. Scale bar, 50 μm. b, Lysotracker Green staining images of U2OS cells after 4 hours treatment with 7.5 μM LY2835219. Experiments were performed three times with similar results. Scale bars, 50 μm. c, Western Blotting analysis for U2OS/mCherry-FUS-ERGmut treated with 7.5 μM LY2835219 for indicated time. Fusion proteins were detected with anti-mCherry antibodies, and GAPDH was used as a loading control. Experiments were performed three times with similar results. GAPDH was used as a loading control. Experiments were performed three times with similar results.

Source data

Extended Data Fig. 8 PSmut of FUS-ERG failed to phase separation and destroy the transcription activation.

a, Live-cell images of EWS-FLI1, FUS-ERG and their PSmut and DBDmut fusion proteins in 293 T cells. Experiments were performed three times with similar results. Scale bars, 10 μm. b, Results of the dual luciferase reporter assay in cells treated with or without 7.5 μM LY2835219 for 6hs. HEK293T cells were transfected with the dual luciferase reporter plasmid and the expression vector for the mCherry-FUS-ERG and mCherry-FUS-ERG mutant fusion protein, mCherry without fusion protein as negative control. Six repeats per group. Two-tailed independent samples t-test. Data are presented as mean ± SD. c, Schematic depicting how PS-DBD fusion proteins drive aberrant phase separation in the related cancers. Under normal conditions, genes undergo typical expression; when a chromosomal rearrangement occurs to create an in-frame PS-BDB fusion, phase separation at the target DNA sites leads to aberrant gene expression, which is correlated with cancer. Small molecules that dissolve the phase-separated condensates can rescue the abnormal expression.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplementary Table 1

Landscape of PS–DBD fusions in public databases.

Supplementary Table 2

Screening results of the DropScan pipeline.

Supplementary Table 3

Transcriptome profiles of A673 cell lines before and after treatment with LY2835219.

Supplementary Table 4

Transcriptome profiles of U2OS/mCherry–EWS–FLI1 cell lines before and after treatment with LY2835219, U2OS/mCherry, U2OS/mCherry–EWS–FLI1mut and U2OS/mCherry–EWS(YS37)–FLI1 cell lines.

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Wang, Y., Yu, C., Pei, G. et al. Dissolution of oncofusion transcription factor condensates for cancer therapy. Nat Chem Biol 19, 1223–1234 (2023). https://doi.org/10.1038/s41589-023-01376-5

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