Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pharmacological targeting of IRF4 as a therapeutic strategy for multiple myeloma

Nature Chemical Biology (2026) Cite this article

Subjects

Abstract

Interferon regulatory factor 4 (IRF4) is an oncogenic transcription factor (TF) in several hematological malignancies. To date, no pharmacological agents have been developed specifically for IRF4 due to the challenging nature of targeting TFs. Here we first identified (S)-H1, a binder of IRF4, by targeting the SPI1−IRF4 interaction on IRF4’s interferon association domain via high-throughput screening. Next, we successfully turned our binder into dIRF4-2, a first-in-class proteolysis-targeting chimera of IRF4, by linking (S)-H1 to E3 ligase ligands of cereblon. dIRF4-2 can induce highly selective proteasomal degradation of IRF4 and has strong cytotoxic effects in all multiple myeloma lines evaluated in vitro. Our study showcases methodology to effectively target the IRF family of TFs and illustrates how to convert an inert binder into a powerful chemical probe for studying the functions of important oncoproteins that are structurally difficult to target.

The alternative text for this image may have been generated using AI.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Discovery of H1 as a ligand of the IRF4 IAD.
The alternative text for this image may have been generated using AI.
Fig. 2: Validation of H1 as a binder of IRF4.
The alternative text for this image may have been generated using AI.
Fig. 3: (S)-H1 is a submicromolar binder of IRF4 with a putative binding site near E295.
The alternative text for this image may have been generated using AI.
Fig. 4: Development and characterization of dIRF4-2 in the MM cell lines MM1.S and OPM2.
The alternative text for this image may have been generated using AI.
Fig. 5: dIRF4-2 results in IRF4-dependent transcriptional changes and is cytotoxic across MM cell models.
The alternative text for this image may have been generated using AI.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or Supplementary Information. Additional raw data, including drug screening data and proteomics datasets, are included as source data at the time of submission. RNA-seq datasets were submitted and are availabe under PRJNA1399318. Structural data around human IRF4 were submitted to the PDB as 9CUG. In case of further questions, please contact the corresponding author. Source data are provided with this paper.

References

  1. Thompson, C. D., Matta, B. & Barnes, B. J. Therapeutic targeting of IRFs: pathway-dependence or structure-based? Front. Immunol. 9, 2622 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Matta, B., Song, S., Li, D. & Barnes, B. J. Interferon regulatory factor signaling in autoimmune disease. Cytokine 98, 15–26 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tao, Z. & Wu, X. Targeting transcription factors in cancer: from ‘undruggable’ to ‘druggable’. Methods Mol. Biol. 2594, 107–131 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bushweller, J. H. Targeting transcription factors in cancer—from undruggable to reality. Nat. Rev. Cancer 19, 611–624 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hu, X., Li, J., Fu, M., Zhao, X. & Wang, W. The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct. Target. Ther. 6, 402 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Xu, H. G., Chen, C., Chen, L. Y. & Pan, S. Pan-cancer analysis identifies the IRF family as a biomarker for survival prognosis and immunotherapy. J. Cell. Mol. Med. 28, e18084 (2024).

    Article  CAS  PubMed  Google Scholar 

  7. Agnarelli, A., Chevassut, T. & Mancini, E. J. IRF4 in multiple myeloma—biology, disease and therapeutic target. Leuk. Res. 72, 52–58 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Shaffer, A. L. et al. IRF4 addiction in multiple myeloma. Nature 454, 226–231 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Salaverria, I. et al. Translocations activating IRF4 identify a subtype of germinal center-derived B-cell lymphoma affecting predominantly children and young adults. Blood 118, 139–147 (2011).

    Article  CAS  PubMed  Google Scholar 

  10. Sciammas, R. et al. Graded expression of interferon regulatory factor-4 coordinates isotype switching with plasma cell differentiation. Immunity 25, 225–236 (2006).

    Article  CAS  PubMed  Google Scholar 

  11. Iida, S. et al. Deregulation of MUM1/IRF4 by chromosomal translocation in multiple myeloma. Nat. Genet. 17, 226–230 (1997).

    Article  CAS  PubMed  Google Scholar 

  12. Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. de Matos Simoes, R. et al. Genome-scale functional genomics identify genes preferentially essential for multiple myeloma cells compared to other neoplasias. Nat. Cancer 4, 754–773 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen, W. & Royer Jr, W. E. Structural insights into interferon regulatory factor activation. Cell Signal. 22, 883–887 (2010).

    Article  CAS  PubMed  Google Scholar 

  15. Remesh, S. G., Santosh, V. & Escalante, C. R. Structural studies of IRF4 reveal a flexible autoinhibitory region and a compact linker domain. J. Biol. Chem. 290, 27779–27790 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Glasmacher, E. et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1−IRF complexes. Science 338, 975–980 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ochiai, K. et al. Zinc finger−IRF composite elements bound by Ikaros/IRF4 complexes function as gene repression in plasma cell. Blood Adv. 2, 883–894 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Brass, A. L., Zhu, A. Q. & Singh, H. Assembly requirements of PU.1-Pip (IRF-4) activator complexes: inhibiting function in vivo using fused dimers. EMBO J. 18, 977–991 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Care, M. A. et al. SPIB and BATF provide alternate determinants of IRF4 occupancy in diffuse large B-cell lymphoma linked to disease heterogeneity. Nucleic Acids Res. 42, 7591–7610 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gupta, S., Jiang, M., Anthony, A. & Pernis, A. B. Lineage-specific modulation of interleukin 4 signaling by interferon regulatory factor 4. J. Exp. Med. 190, 1837–1848 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lodie, T. A. et al. Stimulation of macrophages by lipopolysaccharide alters the phosphorylation state, conformation, and function of PU.1 via activation of casein kinase II. J. Immunol. 158, 1848–1856 (1997).

    Article  CAS  PubMed  Google Scholar 

  22. Yamanaka, S. et al. Lenalidomide derivatives and proteolysis-targeting chimeras for controlling neosubstrate degradation. Nat. Commun. 14, 4683 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Békés, M., Langley, D. R. & Crews, C. M. PROTAC targeted protein degraders: the past is prologue. Nat. Rev. Drug Discov. 21, 181–200 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rengarajan, J. et al. Interferon regulatory factor 4 (IRF4) interacts with NFATc2 to modulate interleukin 4 gene expression. J. Exp. Med. 195, 1003–1012 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Payne, N. C., Kalyakina, A. S., Singh, K., Tye, M. A. & Mazitschek, R. Bright and stable luminescent probes for target engagement profiling in live cells. Nat. Chem. Biol. 17, 1168–1177 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang, J. H., Chung, T. D. & Oldenburg, K. R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 67−73 (1999).

    Article  Google Scholar 

  27. Baell, J. B. & Nissink, J. W. M. Seven year itch: Pan-Assay Interference Compounds (PAINS) in 2017—utility and limitations. ACS Chem. Biol. 13, 36–44 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Yuan, L., Su, Y., Cong, H., Yu, B. & Shen, Y. Application of multifunctional small molecule fluorescent probe BODIPY in life science. Dyes Pigm. 208, 110851 (2023).

    Article  Google Scholar 

  29. Dale, N. C., Johnstone, E. K. M., White, C. W. & Pfleger, K. D. G. NanoBRET: the bright future of proximity-based assays. Front. Bioeng. Biotechnol. 7, 56 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Carotta, S. et al. The transcription factors IRF8 and PU.1 negatively regulate plasma cell differentiation. J. Exp. Med. 211, 2169–2181 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pettersson, M., Sundström, C., Nilsson, K. & Larsson, L. G. The hematopoietic transcription factor PU.1 is downregulated in human multiple myeloma cell lines. Blood 86, 2747–2753 (1995).

    Article  CAS  PubMed  Google Scholar 

  32. Song, S. et al. Inhibition of IRF5 hyperactivation protects from lupus onset and severity. J. Clin. Invest. 130, 6700–6717 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Takahasi, K. et al. Ser386 phosphorylation of transcription factor IRF-3 induces dimerization and association with CBP/p300 without overall conformational change. Genes Cells 15, 901–910 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Burton, N. R., Kim, P. & Backus, K. M. Photoaffinity labelling strategies for mapping the small molecule−protein interactome. Org. Biomol. Chem. 19, 7792–7809 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Molecular Operating Environment (MOE). Chemical Computing Group ULC, 910-1010. https://www.chemcomp.com/en/Products.htm (2024).

  36. Fedele, P. L. et al. The transcription factor IRF4 represses proapoptotic BMF and BIM to licence multiple myeloma survival. Leukemia 35, 2114–2118 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Bricelj, A. et al. Influence of linker attachment points on the stability and neosubstrate degradation of cereblon ligands. ACS Med. Chem. Lett. 12, 1733–1738 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nguyen, T. M. et al. Proteolysis-targeting chimeras with reduced off-targets. Nat. Chem. 16, 218−228 (2024).

  39. Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Zhu, Y. X. et al. Identification of lenalidomide resistance pathways in myeloma and targeted resensitization using cereblon replacement, inhibition of STAT3 or targeting of IRF4. Blood Cancer J. 9, 19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Burslem, G. M. et al. Efficient synthesis of immunomodulatory drug analogues enables exploration of structure-degradation relationships. ChemMedChem 13, 1508–1512 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sellar, R.S. et al. Degradation of GSPT1 causes TP53-independent cell death in leukemia while sparing normal hematopoietic stem cells. J. Clin. Invest. 132, e153514 (2022).

  43. Steger, M. et al. Unbiased mapping of cereblon neosubstrate landscape by high-throughput proteomics. Nat. Commun 16, 7773 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wedel, J. et al. Neuropilin-2 functions as a coinhibitory receptor to regulate antigen-induced inflammation and allograft rejection. J. Clin. Invest. 135, e172218 (2025).

  45. Wong, R. W. J. et al. Feed-forward regulatory loop driven by IRF4 and NF-κB in adult T-cell leukemia/lymphoma. Blood 135, 934–947 (2020).

    Article  PubMed  Google Scholar 

  46. Chen, M. et al. Dynamic single-cell RNA-seq analysis reveals distinct tumor program associated with microenvironmental remodeling and drug sensitivity in multiple myeloma. Cell Biosci. 13, 19 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fedele, P. L. et al. IMiDs prime myeloma cells for daratumumab-mediated cytotoxicity through loss of Ikaros and Aiolos. Blood 132, 2166–2178 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Toriki, E. S. et al. Rational chemical design of molecular glue degraders. ACS Cent. Sci. 9, 915–926 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ting, P. Y. et al. A molecular glue degrader of the WIZ transcription factor for fetal hemoglobin induction. Science 385, 91–99 (2024).

    Article  CAS  PubMed  Google Scholar 

  50. Ueno, S. et al. PU.1 induces apoptosis in myeloma cells through direct transactivation of TRAIL. Oncogene 28, 4116−4125 (2009).

  51. Ueno, N. et al. PU.1 acts as tumor suppressor for myeloma cells through direct transcriptional repression of IRF4. Oncogene 36, 4481–4497 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Xu, W. D., Pan, H. F., Ye, D. Q. & Xu, Y. Targeting IRF4 in autoimmune diseases. Autoimmun. Rev. 11, 918–924 (2012).

    Article  CAS  PubMed  Google Scholar 

  53. Alvisi, G. et al. IRF4 instructs effector Treg differentiation and immune suppression in human cancer. J. Clin. Invest. 130, 3137–3150 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Adamaki, M. et al. Implication of IRF4 aberrant gene expression in the acute leukemias of childhood. PLoS ONE 8, e72326 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mondala, P. K. et al. Selective antisense oligonucleotide inhibition of human IRF4 prevents malignant myeloma regeneration via cell cycle disruption. Cell Stem Cell 28, 623–636 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sheykhhasan, M. et al. CAR T therapies in multiple myeloma: unleashing the future. Cancer Gene Ther. 31, 667–686 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lopez-Girona, A. et al. Lenalidomide downregulates the cell survival factor, interferon regulatory factor-4, providing a potential mechanistic link for predicting response. Br. J. Haematol. 154, 325–336 (2011).

    Article  CAS  PubMed  Google Scholar 

  58. Harada, T. et al. Novel antimyeloma therapeutic option with inhibition of the HDAC1-IRF4 axis and PIM kinase. Blood Adv. 7, 1019–1032 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Agnarelli, A. et al. Dissecting the impact of bromodomain inhibitors on the Interferon Regulatory Factor 4-MYC oncogenic axis in multiple myeloma. Hematol. Oncol. 40, 417–429 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Harberts, A. et al. Interferon regulatory factor 4 controls effector functions of CD8+ memory T cells. Proc. Natl Acad. Sci. USA 118, e2014553118 (2021).

  61. Wurz, R. P. et al. Affinity and cooperativity modulate ternary complex formation to drive targeted protein degradation. Nat. Commun. 14, 4177 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gourisankar, S. et al. Rewiring cancer drivers to activate apoptosis. Nature 620, 417−425 (2023).

  63. Raina, K. et al. Regulated induced proximity targeting chimeras—RIPTACs—a heterobifunctional small molecule strategy for cancer selective therapies. Cell Chem. Biol. 31, 1490–1502 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D Struct. Biol. 75, 861–877 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).

    Article  CAS  PubMed  Google Scholar 

  69. Peng, J. & Gygi, S. P. Proteomics: the move to mixtures. J. Mass Spectrom. 36, 1083–1091 (2001).

    Article  CAS  PubMed  Google Scholar 

  70. Navarrete-Perea, J., Yu, Q., Gygi, S. P. & Paulo, J. A. Streamlined tandem mass tag (SL-TMT) protocol: an efficient strategy for quantitative (phospho)proteome profiling using tandem mass tag-synchronous precursor selection-MS3. J. Proteome Res. 17, 2226–2236 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Li, J. et al. TMTpro-18plex: the expanded and complete set of TMTpro reagents for sample multiplexing. J. Proteome Res. 20, 2964–2972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Schweppe, D. K. et al. Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics. J. Proteome Res. 19, 2026–2034 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Rad, R. et al. Improved monoisotopic mass estimation for deeper proteome coverage. J. Proteome Res. 20, 591–598 (2021).

    Article  CAS  PubMed  Google Scholar 

  75. Parkhomchuk, D. et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  77. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  78. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  81. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the members of the Structure Biology Core at Dana-Farber Cancer Institute for their technical assistance; the Linde Family Foundation and the Novartis Biomedical Research DDTRP program for supporting S.D.-P., I.M.G. and J.Q.; the Leukemia & Lymphoma Society (now Blood Cancer United) for supporting J.Q.; Northeastern Collaborative Access Team beamlines (synchrotron); and the Thermo Fisher Scientific Center for Multiplexed Proteomics at Harvard Medical School (proteomics). We also thank J. Smith, J. Splaine, D. Wrobel and A. Morton for ICCB screening support; N. Davey for peptide identification; M. Slabicki for the GSPT1 mutant plasmids; A. Sperling for the CRBN-KO MM1.S cell models; and C. Escalante for providing reagents.

Author information

Author notes

  1. These authors contributed equally: Michael P. Agius, Chen Song.

  2. These authors jointly supervised this work: Irene M. Ghobrial, Jun Qi.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Michael P. Agius, Tomoki Iemura, Laura Hevenor, Romanos Sklavenitis Pistofidis, Lorena Pantano, Daniel Heilpern-Mallory, Puspalata Bashyal, Michelle P. Aranha, Elizabeth Lightbody, Constantine S. Mitsiades & Irene M. Ghobrial

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Michael P. Agius, Chen Song, Qi Liu, Tomoki Iemura, Laura Hevenor, Romanos Sklavenitis Pistofidis, Lorena Pantano, Hongfang Zhao, Daniel Heilpern-Mallory, Caleb Heaslip, Puspalata Bashyal, Michelle P. Aranha, Elizabeth Lightbody, Constantine S. Mitsiades, Irene M. Ghobrial & Jun Qi

  3. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Chen Song, Qi Liu, Hongfang Zhao, Caleb Heaslip, Zhen-Yu J. Sun & Jun Qi

  4. Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA

    N. Connor Payne & Ralph Mazitschek

  5. Harvard Department of Chemistry and Chemical Biology, Cambridge, MA, USA

    N. Connor Payne

  6. Harvard T. H. Chan School of Public Health, Boston, MA, USA

    N. Connor Payne & Ralph Mazitschek

  7. Chemical Biology Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Hyuk-Soo Seo & Sirano Dhe-Paganon

  8. Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Hyuk-Soo Seo & Sirano Dhe-Paganon

  9. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ralph Mazitschek

Authors
  1. Michael P. Agius
    View author publications

    Search author on:PubMed Google Scholar

  2. Chen Song
    View author publications

    Search author on:PubMed Google Scholar

  3. Qi Liu
    View author publications

    Search author on:PubMed Google Scholar

  4. Tomoki Iemura
    View author publications

    Search author on:PubMed Google Scholar

  5. Laura Hevenor
    View author publications

    Search author on:PubMed Google Scholar

  6. N. Connor Payne
    View author publications

    Search author on:PubMed Google Scholar

  7. Romanos Sklavenitis Pistofidis
    View author publications

    Search author on:PubMed Google Scholar

  8. Lorena Pantano
    View author publications

    Search author on:PubMed Google Scholar

  9. Hongfang Zhao
    View author publications

    Search author on:PubMed Google Scholar

  10. Hyuk-Soo Seo
    View author publications

    Search author on:PubMed Google Scholar

  11. Daniel Heilpern-Mallory
    View author publications

    Search author on:PubMed Google Scholar

  12. Caleb Heaslip
    View author publications

    Search author on:PubMed Google Scholar

  13. Zhen-Yu J. Sun
    View author publications

    Search author on:PubMed Google Scholar

  14. Puspalata Bashyal
    View author publications

    Search author on:PubMed Google Scholar

  15. Michelle P. Aranha
    View author publications

    Search author on:PubMed Google Scholar

  16. Elizabeth Lightbody
    View author publications

    Search author on:PubMed Google Scholar

  17. Ralph Mazitschek
    View author publications

    Search author on:PubMed Google Scholar

  18. Sirano Dhe-Paganon
    View author publications

    Search author on:PubMed Google Scholar

  19. Constantine S. Mitsiades
    View author publications

    Search author on:PubMed Google Scholar

  20. Irene M. Ghobrial
    View author publications

    Search author on:PubMed Google Scholar

  21. Jun Qi
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization was contributed by M.P.A., I.M.G. and J.Q. Experimental methodology was contributed by M.P.A., C.S., I.M.G. and J.Q. Assay development using CoraFluor TR-FRET technology was contributed by N.C.P. and R.M. Structural characterization and protein production were contributed by P.B., S.D.-P., Z.-Y.J.S. and H.-S.S. Biological evaluation was contributed by M.P.A., C.S., T.I., L.H., H.Z., D.H.-M., C.H., E.L., C.M. and I.M.G. Chemical synthesis and probe design were contributed by M.P.A., Q.L. and J.Q. Computational analysis was supported by R.S.P., L.P. and M.P.A. Funding acquisition was contributed by M.P.A., I.M.G. and J.Q. Project administration was contributed by M.P.A., I.M.G. and J.Q. Supervision was provided by I.M.G. and J.Q. Writing the original draft was completed by M.P.A., I.M.G. and J.Q. Q.L. and T.I. contributed equally as second authors. Reviewing and editing were completed by M.P.A., C.S., Q.L., T.I., L.H., N.P., R.S.P., L.P., H.Z., H.-S.S., D.H.-M., C.H., Z.-Y.J.S., P.B., M.P.A., E.L., R.M., S.D.-P., C.S.M., I.M.G. and J.Q. (all authors).

Corresponding authors

Correspondence to Irene M. Ghobrial or Jun Qi.

Ethics declarations

Competing interests

R.M. and N.C.P. are inventors on patent applications related to the CoraFluor TR-FRET technology used in this work (US 2022/002254). R.S.P. is a consultant, equity holder and co-founder of Predicta Biosciences. C.S.M. has served on the Scientific Advisory Board of Adicet Bio and discloses consultant/honoraria from Ionis, Genentech, Nerviano, Secura Bio and Oncopeptides, and research funding from EMD Serono, Karyopharm, Sanofi, Nurix, BMS, H3 Biomedicine/Eisai, Springworks, Abcuro, Novartis and OPNA. I.M.G. has consulting or advisory roles with AbbVie, Adaptive, Amgen, Aptitude Health, Bristol Myers Squibb, GlaxoSmithKline, Huron Consulting, Janssen, Menarini Silicon Biosystems, Oncopeptides, Pfizer, Sanofi, Sognef, Takeda, The Binding Site and Window Therapeutics; has received speaker fees from Vor Biopharma and Veeva Systems; is a co-founder, equity holder and consultant for PreDICTA Biosciences; and her spouse is the CMO and an equity holder of Disc Medicine. J.Q. is a scientific co-founder and shareholder of Epiphanes, Inc. The other authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Lanbo Xiao and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 (S)-H1 as a Validated Binder of IRF4 and its Putative Binding Site on the IAD Domain.

(A) 15 N-TROSY HSQC spectra of 40 uM 15 N-IRF4-IAD alone (blue), with 80 uM unlabeled P1 (red), and with 200uM (S)-H1 (green). Several of the most shifted peaks show shared chemical shifts after incubation of P1 and H1. (B) Top docking pose of photoaffinity probe (S)-H1-C into the putative binding pocket that was identified by mass spectrometry. Residues E259 and Q252 are highlighted in red. (C) Bottom facing pose (left) and surface representation (right) display the photoaffinity diazirine to be in the proximity of E259 which increases confidence of this binding site.

Source data

Extended Data Fig. 2 Evaluation of dIRF4-1 in MM1.S.

(A) Structural information of dIRF4-1, dIRF4-1-Me, and inact-dIRF4-1 which utilizes the CRBN linker linked to the 3’-phenyl glycine ring. (B) (Top) Both dIRF4-1 and dIRF4-1-Me were evaluted in MM1.S cells for IRF4 degradation activity after treatment of 24 hr treatment. Cell were harvested and IRF4 and levels analyzed by Western Blot (n = 2 replicates). (Bottom) Degradation of IRF4 by dIRF4-2 was evaluated in WT-MM1.S and CRBN-KO MM1.S cell lines after 24 hr treatment. Cell were harvested and IRF4 and CRBN levels analyzed by Western Blot (n = 2 replicates). (C) (Top) Cytotoxicity of dIRF4-1 in a CRBN CRISPR-KO MM1.S cell model with dIRF4-1 and proliferation was measured using cell-titer glo (Promega) after 24 hr incubation (n = 3) from 0 - 100 µM doses. MM1.S cells were treated with dIRF4-1 or its control compounds, and proliferation was measured using cell-titer glo (Promega) after 24 h incubation (n = 3) up to 100 µM. (Bottom) Cytotoxicity of dIRF4-1, dIRF4-1-Me, and Inact-dIRF4-1 against MM1.S and a MM1S WT cell line. Cells were treated with dIRF4-1 or its control compounds, and proliferation was measured using cell-titer glo (Promega) after 24 h incubation (n = 3) up to 100 µM. (D) Proteomic analysis of dIRF4-1 in MM1.S. Cells were treated with either 25 µM dIRF4-1 or DMSO for 24 hr in n = 4 biological replicates. Cells were harvested and prepared for proteomics using standard lysis, trypsin, and labeling with TMT. Volcano plot depicts significant (p < 0.05) up- (red dots) and down-regulated (blue dots) proteins by dIRF4-1. Other IRF isoforms (black dots) and CRBN neosubstrates (purple dots) show no significant changes. Dashed lines represent p-value = 0.05 and fold changes ± 1.5x.

Source data

Extended Data Fig. 3 Proteomic, Transcriptomic, and IRF8 Selectivity Evaluation of dIRF4-2.

(A) Proteomic analysis of dIRF4-2 in MM1.S. Cells were treated with either 2.5 µM dIRF4-2 or DMSO for 6 hr in n = 3 biological replicates. Cells were harvested and prepared for proteomics using standard lysis, trypsin, and labeling with TMT. Volcano plot depicts significant (p < 0.05) up- (red dots) and down-regulated (blue dots) proteins by dIRF4-2. Other IRF isoforms and CRBN neosubstrates (black dots) show no significant changes. Dashed lines represent p-value = 0.05 and fold changes ± 2x. (B) Volcano-plot of bulk RNA sequencing performed on MM1.S cell treated with 2.5 µM dIRF4-2 or DMSO (n = 3) after 24 hr depicts 1,006 significantly dysregulated genes (p < 0.05). (C) HALLMARK pathway analysis of the downregulated and upregulated genes respectfully (q < 0.01). (D) Off-target assessment of IRF8 by Western Blot. RPMI-8226 cells were treated with dIRF4-2 or DMSO for 24 hr (n = 2 replicates). Cell were harvested and IRF4 and IRF8 levels analyzed by Western Blot.

Source data

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1−15, Supplementary Tables 1 and 2 and Synthetic schemes: ‘Synthetic Schemes for Ligands, Degraders and Probes’.

Reporting Summary (download PDF )

Supplementary Data (download ZIP )

Uncropped blots for Supplementary Information.

Source data

Source Data Fig. 1 (download ZIP )

Raw fluorescence polarization and TR-FRET data (c); drug screening data (d); TR-FRET IC50 values (f).

Source Data Fig. 2 (download ZIP )

ITC data (a); raw blots (b); raw fluorescence polarization and TR-FRET data (c); raw BRET data (e,f).

Source Data Fig. 3 (download ZIP )

Raw TR-FRET data (b); PyMOL overlays (c); PyMOL from docking studies (e).

Source Data Fig. 4 (download ZIP )

Raw blots (b,d,e); TMT proteomics source data (c); uncropped microscopy data (f); processed statistical source data for Quant (f).

Source Data Fig. 5 (download ZIP )

Cell proliferation source data (a); processed RNA-seq data (b); GSEA (c,d).

Source Data Extended Data Fig. 1 (download ZIP )

Unprocessed nuclear magnetic resonance data (a); PyMOL docking models (b,c).

Source Data Extended Data Fig. 2 (download ZIP )

Unprocessed blots (b); raw proliferation source data (c); TMT proteomics source data (d).

Source Data Extended Data Fig. 3 (download ZIP )

TMT proteomics source data (a); processed RNA-seq data (b); GSEA (c); unprocessed blot (d).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agius, M.P., Song, C., Liu, Q. et al. Pharmacological targeting of IRF4 as a therapeutic strategy for multiple myeloma. Nat Chem Biol (2026). https://doi.org/10.1038/s41589-026-02228-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41589-026-02228-8

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer